Impact of organic fertilizers on the quality of mango (Mangifera indica L.) var. ‘Kent’ during physiological maturity and commercial maturity

1 Project Setup

library(emmeans)
library(corrplot)
library(multcomp)
library(FSA)
library(factoextra)
library(corrplot)
library(car)
source('https://inkaverse.com/setup.r')

cat("Project: ", getwd(), "\n")

Project:  C:/INIA/GIT/prochira_abonos_mango 

session_info()

─ Session info ───────────────────────────────────────────────────────────────
 setting  value
 version  R version 4.5.0 (2025-04-11 ucrt)
 os       Windows 11 x64 (build 26100)
 system   x86_64, mingw32
 ui       RTerm
 language (EN)
 collate  Spanish_Peru.utf8
 ctype    Spanish_Peru.utf8
 tz       America/Lima
 date     2025-06-17
 pandoc   3.4 @ C:/Program Files/RStudio/resources/app/bin/quarto/bin/tools/ (via rmarkdown)
 quarto   NA @ C:\\PROGRA~1\\RStudio\\RESOUR~1\\app\\bin\\quarto\\bin\\quarto.exe

─ Packages ───────────────────────────────────────────────────────────────────
 package       * version date (UTC) lib source
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 agricolae       1.3-7   2023-10-22 [1] CRAN (R 4.5.0)
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 forcats       * 1.0.0   2023-01-29 [1] CRAN (R 4.5.0)
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 lme4          * 1.1-37  2025-03-26 [1] CRAN (R 4.5.0)
 lubridate     * 1.9.4   2024-12-08 [1] CRAN (R 4.5.0)
 magick        * 2.8.6   2025-03-23 [1] CRAN (R 4.5.0)
 magrittr        2.0.3   2022-03-30 [1] CRAN (R 4.5.0)
 MASS          * 7.3-65  2025-02-28 [2] CRAN (R 4.5.0)
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 pillar          1.10.2  2025-04-05 [1] CRAN (R 4.5.0)
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 [1] C:/Users/INIA/AppData/Local/R/win-library/4.5
 [2] C:/Program Files/R/R-4.5.0/library
 * ── Packages attached to the search path.

──────────────────────────────────────────────────────────────────────────────

2 Import data

Data was imported from the field book evaluated during the 2022-2023 growing season. The evaluations focused on mango fruits of the ‘Kent’ variety at two stages: physiological maturity and commercial maturity.

url <- "https://docs.google.com/spreadsheets/d/1cjWrS-EVcII85c-l_NuEfTpjhVMI156e8REM9GDVP_w/edit?gid=95386135#gid=95386135"

gs <- url %>% 
  as_sheets_id()

tratamiento <- gs %>% 
  range_read("tratamientos") %>% 
  rename_with(~ tolower(.))

rendimiento <- gs %>% 
  range_read("rendimiento") %>% 
  rename_with(~ tolower(.)) 

fisio <- gs %>% 
  range_read("fisio") %>% 
  rename_with(~ tolower(.)) %>% 
  merge(., tratamiento) %>% 
  dplyr::select(tratamiento,compost, biol,everything()) %>% 
  merge(., rendimiento) %>% 
  mutate(across(tratamiento:nfrutos, ~ as.factor(.))) %>% 
  rename(treat = tratamiento
         , repetition = repeticion
         , composts = compost)

str(fisio)
## 'data.frame':    405 obs. of  15 variables:
##  $ treat     : Factor w/ 9 levels "T0","T1","T2",..: 1 1 1 1 1 1 1 1 1 1 ...
##  $ repetition: Factor w/ 3 levels "1","2","3": 1 1 1 1 1 1 1 1 1 1 ...
##  $ nplantas  : Factor w/ 3 levels "1","2","3": 1 1 1 1 1 2 2 2 2 2 ...
##  $ composts  : Factor w/ 3 levels "0","5","15": 1 1 1 1 1 1 1 1 1 1 ...
##  $ biol      : Factor w/ 3 levels "0","5","10": 1 1 1 1 1 1 1 1 1 1 ...
##  $ nfrutos   : Factor w/ 5 levels "1","2","3","4",..: 1 2 3 4 5 1 2 3 4 5 ...
##  $ pcfmf     : num  90 70 60 40 70 70 40 60 30 80 ...
##  $ ffmf      : num  13.2 12 10 10.2 12.8 11.6 10.8 11.9 11 11.2 ...
##  $ cifmf     : num  2 2 2 2 2 1.5 2 2 2.5 2.5 ...
##  $ ssfmf     : num  8.8 8.6 8.5 8.2 8.6 9.8 9.7 9.4 9.2 8.5 ...
##  $ phfmf     : num  2.6 2.55 2.52 2.58 2.55 ...
##  $ atfmf     : num  1.39 1.38 1.2 1.12 1.35 1.49 1.29 1.52 1.54 1.42 ...
##  $ msfmf     : num  19.1 19.1 19.1 19.1 19.1 ...
##  $ imf       : num  6.33 6.23 7.08 7.32 6.37 6.58 7.52 6.18 5.97 5.99 ...
##  $ rpp       : num  51.3 51.3 51.3 51.3 51.3 52.4 52.4 52.4 52.4 52.4 ...

consumo <- gs %>% 
  range_read("consumo") %>% 
  rename_with(~ tolower(.)) %>%
  merge(., tratamiento) %>% 
  dplyr::select(tratamiento,compost, biol,everything()) %>% 
  mutate(across(tratamiento:nfrutos, ~ as.factor(.))) %>% 
    rename(treat = tratamiento
         , repetition = repeticion
         , composts = compost
         , n_fruits = nfrutos)

glimpse(consumo)
## Rows: 135
## Columns: 12
## $ treat      <fct> T0, T0, T0, T0, T0, T0, T0, T0, T0, T0, T0, T0, T0, T0, T0,…
## $ composts   <fct> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 5, 5, 5, 5,…
## $ biol       <fct> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
## $ repetition <fct> 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 3, 3, 3, 3, 3, 1, 1, 1, 1, 1,…
## $ n_fruits   <fct> 1, 2, 3, 4, 5, 1, 2, 3, 4, 5, 1, 2, 3, 4, 5, 1, 2, 3, 4, 5,…
## $ pdfmc      <dbl> 6.68, 6.78, 7.02, 6.78, 6.12, 6.62, 6.91, 7.04, 6.45, 6.50,…
## $ ffmc       <dbl> 3.0, 3.0, 4.0, 3.8, 4.2, 4.0, 3.6, 3.0, 3.0, 3.0, 3.0, 3.4,…
## $ cifmc      <dbl> 3.0, 3.0, 3.0, 3.0, 3.0, 3.0, 3.5, 3.0, 3.0, 3.0, 3.5, 3.0,…
## $ ssfmc      <dbl> 14.6, 15.6, 14.8, 15.0, 14.0, 14.4, 15.5, 15.1, 15.2, 15.2,…
## $ phfmc      <dbl> 4.22, 4.19, 4.21, 4.15, 4.26, 4.34, 4.35, 4.34, 4.30, 4.38,…
## $ atfmc      <dbl> 0.600, 0.700, 0.700, 0.500, 0.500, 0.605, 0.615, 0.625, 0.6…
## $ imf        <dbl> 24.33333, 22.28571, 21.14286, 30.00000, 28.00000, 23.80165,…

rendimiento %>% kable(caption = "Yield related traits")

Yield related traits

tratamiento	repeticion	nplantas	rpp
T0	1	1	51.30
T0	1	2	52.40
T0	1	3	49.20
T0	2	1	43.40
T0	2	2	45.24
T0	2	3	46.06
T0	3	1	51.72
T0	3	2	51.10
T0	3	3	50.72
T1	1	1	59.00
T1	1	2	56.80
T1	1	3	58.56
T1	2	1	76.20
T1	2	2	74.00
T1	2	3	75.76
T1	3	1	107.00
T1	3	2	106.20
T1	3	3	107.00
T2	1	1	121.40
T2	1	2	119.20
T2	1	3	120.70
T2	2	1	91.60
T2	2	2	93.20
T2	2	3	92.58
T2	3	1	100.60
T2	3	2	102.60
T2	3	3	102.14
T3	1	1	47.60
T3	1	2	50.88
T3	1	3	47.80
T3	2	1	36.00
T3	2	2	34.44
T3	2	3	32.88
T3	3	1	40.48
T3	3	2	39.40
T3	3	3	39.92
T4	1	1	53.40
T4	1	2	54.70
T4	1	3	56.60
T4	2	1	56.48
T4	2	2	56.60
T4	2	3	56.46
T4	3	1	50.40
T4	3	2	51.00
T4	3	3	50.20
T5	1	1	107.40
T5	1	2	109.04
T5	1	3	107.48
T5	2	1	82.60
T5	2	2	82.28
T5	2	3	82.00
T5	3	1	63.56
T5	3	2	61.40
T5	3	3	63.66
T6	1	1	74.50
T6	1	2	76.60
T6	1	3	71.90
T6	2	1	123.40
T6	2	2	120.60
T6	2	3	120.68
T6	3	1	72.76
T6	3	2	75.60
T6	3	3	73.40
T7	1	1	110.00
T7	1	2	109.40
T7	1	3	106.20
T7	2	1	104.20
T7	2	2	105.00
T7	2	3	104.40
T7	3	1	107.20
T7	3	2	108.40
T7	3	3	109.40
T8	1	1	119.48
T8	1	2	118.30
T8	1	3	120.10
T8	2	1	126.48
T8	2	2	128.20
T8	2	3	126.80
T8	3	1	94.20
T8	3	2	94.20
T8	3	3	94.30

fisio %>% kable(caption = "Evaluation of fruits at fisiological maturity")

Evaluation of fruits at fisiological maturity

treat	repetition	nplantas	composts	biol	nfrutos	pcfmf	ffmf	cifmf	ssfmf	phfmf	atfmf	msfmf	imf	rpp
T0	1	1	0	0	1	90	13.2	2.0	8.80	2.602942	1.390	19.060	6.33	51.30
T0	1	1	0	0	2	70	12.0	2.0	8.60	2.548103	1.380	19.060	6.23	51.30
T0	1	1	0	0	3	60	10.0	2.0	8.50	2.520683	1.200	19.060	7.08	51.30
T0	1	1	0	0	4	40	10.2	2.0	8.20	2.575522	1.120	19.060	7.32	51.30
T0	1	1	0	0	5	70	12.8	2.0	8.60	2.548103	1.350	19.060	6.37	51.30
T0	1	2	0	0	1	70	11.6	1.5	9.80	2.877140	1.490	18.400	6.58	52.40
T0	1	2	0	0	2	40	10.8	2.0	9.70	2.849720	1.290	18.400	7.52	52.40
T0	1	2	0	0	3	60	11.9	2.0	9.40	2.767461	1.520	18.400	6.18	52.40
T0	1	2	0	0	4	30	11.0	2.5	9.20	2.986819	1.540	18.400	5.97	52.40
T0	1	2	0	0	5	80	11.2	2.5	8.50	2.520683	1.420	18.400	5.99	52.40
T0	1	3	0	0	1	80	12.0	2.5	9.60	2.822300	1.430	18.520	6.71	49.20
T0	1	3	0	0	2	50	12.0	2.0	8.00	2.383584	1.480	18.520	5.41	49.20
T0	1	3	0	0	3	80	11.6	2.0	8.00	2.383584	1.250	18.520	6.40	49.20
T0	1	3	0	0	4	70	11.0	2.0	8.50	2.520683	1.420	18.520	5.99	49.20
T0	1	3	0	0	5	60	10.0	2.0	8.60	2.548103	1.350	18.520	6.37	49.20
T0	2	1	0	0	2	60	9.4	2.0	8.40	2.383584	1.560	18.540	5.38	43.40
T0	2	1	0	0	3	80	9.4	2.0	8.80	2.602942	1.480	18.540	5.95	43.40
T0	2	1	0	0	4	80	12.6	2.5	8.20	2.602942	1.350	18.540	6.07	43.40
T0	2	1	0	0	5	60	11.0	2.0	8.30	2.630362	1.240	18.540	6.69	43.40
T0	2	1	0	0	1	80	9.8	2.0	8.00	2.383584	1.420	18.540	5.63	43.40
T0	2	2	0	0	2	60	12.6	2.5	9.10	2.877140	1.190	20.420	7.65	45.24
T0	2	2	0	0	3	50	10.2	2.0	9.20	2.794881	1.570	20.420	5.86	45.24
T0	2	2	0	0	4	80	10.0	2.5	8.20	2.383584	1.540	20.420	5.32	45.24
T0	2	2	0	0	1	70	9.8	2.5	8.00	2.712621	1.150	20.420	6.96	45.24
T0	2	2	0	0	5	20	12.2	2.5	8.40	2.904560	1.480	20.420	5.68	45.24
T0	2	3	0	0	1	60	9.2	2.5	8.00	2.383584	1.250	18.450	6.40	46.06
T0	2	3	0	0	2	10	10.0	2.5	9.20	2.931980	1.260	18.450	7.30	46.06
T0	2	3	0	0	3	80	12.0	2.5	8.40	2.931980	1.280	18.450	6.56	46.06
T0	2	3	0	0	5	70	10.0	2.0	8.40	2.657782	1.350	18.450	6.22	46.06
T0	2	3	0	0	4	80	9.8	2.0	9.20	2.712621	1.240	18.450	7.42	46.06
T0	3	1	0	0	2	20	12.2	2.0	8.45	2.506973	1.360	20.880	6.21	51.72
T0	3	1	0	0	3	80	12.4	2.0	8.35	2.479553	1.480	20.880	5.64	51.72
T0	3	1	0	0	5	50	13.0	2.0	8.45	2.506973	1.520	20.880	5.56	51.72
T0	3	1	0	0	1	20	13.4	1.5	8.65	2.561812	1.450	20.880	5.97	51.72
T0	3	1	0	0	4	60	12.6	2.0	8.55	2.534393	1.490	20.880	5.74	51.72
T0	3	2	0	0	1	70	11.8	3.0	8.00	2.383584	1.240	19.260	6.45	51.10
T0	3	2	0	0	2	60	11.6	2.5	8.00	2.383584	1.260	19.260	6.35	51.10
T0	3	2	0	0	5	30	11.4	2.5	9.00	2.657782	1.320	19.260	6.82	51.10
T0	3	2	0	0	3	50	12.8	2.0	8.00	2.383584	1.280	19.260	6.25	51.10
T0	3	2	0	0	4	80	11.2	3.0	8.20	2.438423	1.350	19.260	6.07	51.10
T0	3	3	0	0	1	10	11.6	2.0	9.00	2.657782	1.340	19.890	6.72	50.72
T0	3	3	0	0	4	30	12.9	2.0	8.00	2.383584	1.310	19.890	6.11	50.72
T0	3	3	0	0	5	20	12.2	2.0	8.00	2.383584	1.200	19.890	6.67	50.72
T0	3	3	0	0	2	50	11.8	2.0	8.40	2.493263	1.560	19.890	5.38	50.72
T0	3	3	0	0	3	20	12.2	2.0	9.00	2.657782	1.290	19.890	6.98	50.72
T1	1	1	5	0	4	40	12.5	2.0	9.20	2.383584	1.290	18.900	7.13	59.00
T1	1	1	5	0	5	50	10.0	2.0	8.00	2.383584	1.360	18.900	5.88	59.00
T1	1	1	5	0	2	60	10.0	2.0	8.20	2.383584	1.340	18.900	6.12	59.00
T1	1	1	5	0	1	80	10.0	2.0	8.50	2.520683	1.280	18.900	6.64	59.00
T1	1	1	5	0	3	30	10.0	2.0	8.40	2.493263	1.250	18.900	6.72	59.00
T1	1	2	5	0	4	90	11.6	2.0	9.00	2.657782	1.280	18.340	7.03	56.80
T1	1	2	5	0	5	80	10.4	2.5	8.80	2.602942	1.190	18.340	7.39	56.80
T1	1	2	5	0	3	30	11.4	2.0	9.60	2.822300	1.290	18.340	7.44	56.80
T1	1	2	5	0	1	70	11.4	2.5	9.50	2.685201	1.470	18.340	6.46	56.80
T1	1	2	5	0	2	50	10.2	2.5	9.40	2.657782	1.280	18.340	7.34	56.80
T1	1	3	5	0	4	60	10.0	2.0	8.80	2.438423	1.350	18.200	6.52	58.56
T1	1	3	5	0	2	80	11.2	2.0	9.20	2.657782	1.260	18.200	7.30	58.56
T1	1	3	5	0	3	70	10.8	2.0	8.60	2.548103	1.290	18.200	6.67	58.56
T1	1	3	5	0	1	90	11.6	2.0	10.00	2.931980	1.240	18.200	8.06	58.56
T1	1	3	5	0	5	70	11.0	2.0	9.80	2.877140	1.150	18.200	8.52	58.56
T1	2	1	5	0	3	70	12.2	2.0	8.00	2.383584	1.280	19.940	6.25	76.20
T1	2	1	5	0	1	90	12.0	2.0	8.80	2.383584	1.350	19.940	6.52	76.20
T1	2	1	5	0	4	40	11.5	2.0	8.50	2.520683	1.270	19.940	6.69	76.20
T1	2	1	5	0	5	70	11.4	2.0	9.70	2.849720	1.240	19.940	7.82	76.20
T1	2	1	5	0	2	60	13.8	2.0	8.20	2.438423	1.320	19.940	6.21	76.20
T1	2	2	5	0	3	50	12.5	2.0	9.10	2.685201	1.480	18.600	6.15	74.00
T1	2	2	5	0	4	30	10.0	1.5	8.20	2.164225	1.250	18.600	6.56	74.00
T1	2	2	5	0	5	80	11.8	2.5	8.20	2.438423	1.340	18.600	6.12	74.00
T1	2	2	5	0	1	60	12.4	1.0	8.60	2.548103	1.250	18.600	6.88	74.00
T1	2	2	5	0	2	20	10.2	2.5	8.80	2.602942	1.260	18.600	6.98	74.00
T1	2	3	5	0	1	50	12.0	2.0	8.20	2.438423	1.350	18.280	6.07	75.76
T1	2	3	5	0	2	80	12.0	2.5	9.00	2.657782	1.310	18.280	6.87	75.76
T1	2	3	5	0	4	70	10.0	2.0	8.40	2.328744	1.250	18.280	6.72	75.76
T1	2	3	5	0	5	10	11.6	2.0	9.80	2.877140	1.290	18.280	7.60	75.76
T1	2	3	5	0	3	20	10.4	2.0	8.70	2.575522	1.250	18.280	6.96	75.76
T1	3	1	5	0	2	60	13.0	2.5	8.00	2.383584	1.350	21.380	5.93	107.00
T1	3	1	5	0	3	80	13.4	2.0	8.25	2.452133	1.340	21.380	6.16	107.00
T1	3	1	5	0	4	70	12.8	2.0	8.20	2.438423	1.390	21.380	5.90	107.00
T1	3	1	5	0	1	80	13.2	2.0	8.35	2.479553	1.280	21.380	6.52	107.00
T1	3	1	5	0	5	60	12.6	2.0	7.30	2.164225	1.410	21.380	5.18	107.00
T1	3	2	5	0	1	50	11.6	2.5	8.10	2.644072	1.350	18.750	6.00	106.20
T1	3	2	5	0	2	80	11.4	2.0	8.40	2.383584	1.240	18.750	6.77	106.20
T1	3	2	5	0	5	30	11.8	2.5	8.50	2.657782	1.350	18.750	6.30	106.20
T1	3	2	5	0	3	70	11.6	2.5	8.80	2.712621	1.250	18.750	7.04	106.20
T1	3	2	5	0	4	40	11.0	2.5	8.60	2.794881	1.380	18.750	6.23	106.20
T1	3	3	5	0	1	80	11.0	2.5	8.90	2.630362	1.340	19.270	6.64	107.00
T1	3	3	5	0	4	80	12.4	2.5	8.00	2.424714	1.200	19.270	6.67	107.00
T1	3	3	5	0	5	50	11.9	2.0	8.20	2.438423	1.190	19.270	6.89	107.00
T1	3	3	5	0	2	50	10.8	2.5	8.20	2.383584	1.260	19.270	6.51	107.00
T1	3	3	5	0	3	90	12.8	2.5	8.10	2.411004	1.270	19.270	6.38	107.00
T2	1	1	15	0	1	60	12.8	2.0	8.80	2.602942	1.340	20.600	6.57	121.40
T2	1	1	15	0	2	70	13.0	2.0	8.60	2.548103	1.150	20.600	7.48	121.40
T2	1	1	15	0	3	50	13.2	2.0	8.50	2.520683	1.290	20.600	6.59	121.40
T2	1	1	15	0	4	80	11.2	2.5	10.20	2.986819	1.420	20.600	7.18	121.40
T2	1	1	15	0	5	70	12.5	2.0	8.40	2.493263	1.470	20.600	5.71	121.40
T2	1	2	15	0	1	90	12.6	2.5	8.90	2.630362	1.280	19.320	6.95	119.20
T2	1	2	15	0	2	90	10.0	2.0	9.00	2.657782	1.290	19.320	6.98	119.20
T2	1	2	15	0	3	100	12.4	2.0	9.00	2.657782	1.280	19.320	7.03	119.20
T2	1	2	15	0	4	90	12.2	2.0	9.50	2.794881	1.190	19.320	7.98	119.20
T2	1	2	15	0	5	80	11.8	2.0	8.80	2.602942	1.240	19.320	7.10	119.20
T2	1	3	15	0	3	60	10.8	2.0	9.00	2.657782	1.250	18.580	7.20	120.70
T2	1	3	15	0	4	10	12.0	2.0	9.40	2.767461	1.150	18.580	8.17	120.70
T2	1	3	15	0	5	80	11.2	2.5	10.50	3.069079	1.280	18.580	8.20	120.70
T2	1	3	15	0	2	50	11.4	2.5	9.00	2.657782	1.290	18.580	6.98	120.70
T2	1	3	15	0	1	70	11.6	2.5	9.00	2.657782	1.160	18.580	7.76	120.70
T2	2	1	15	0	2	70	13.0	2.0	8.60	2.548103	1.280	19.740	6.72	91.60
T2	2	1	15	0	3	60	10.4	2.0	8.50	2.520683	1.290	19.740	6.59	91.60
T2	2	1	15	0	4	50	11.2	2.5	10.20	2.986819	1.280	19.740	7.97	91.60
T2	2	1	15	0	5	70	12.8	2.0	8.40	2.493263	1.190	19.740	7.06	91.60
T2	2	1	15	0	1	70	12.8	2.0	8.80	2.602942	1.350	19.740	6.52	91.60
T2	2	2	15	0	1	70	12.6	1.5	8.90	2.630362	1.340	20.400	6.64	93.20
T2	2	2	15	0	2	90	13.0	2.0	8.60	2.548103	1.420	20.400	6.06	93.20
T2	2	2	15	0	4	30	12.2	2.0	9.00	2.657782	1.220	20.400	7.38	93.20
T2	2	2	15	0	5	60	12.0	2.0	9.00	2.657782	1.270	20.400	7.09	93.20
T2	2	2	15	0	3	100	12.4	2.0	8.90	2.630362	1.240	20.400	7.18	93.20
T2	2	3	15	0	2	50	11.4	2.5	9.80	2.877140	1.280	18.580	7.66	92.58
T2	2	3	15	0	3	40	11.0	2.0	9.00	2.657782	1.190	18.580	7.56	92.58
T2	2	3	15	0	1	70	11.3	2.0	10.00	2.931980	1.290	18.580	7.75	92.58
T2	2	3	15	0	5	80	11.2	2.5	10.20	2.986819	1.290	18.580	7.91	92.58
T2	2	3	15	0	4	70	12.0	2.0	9.00	2.657782	1.260	18.580	7.14	92.58
T2	3	1	15	0	2	40	13.2	2.0	8.45	2.506973	1.290	20.600	6.55	100.60
T2	3	1	15	0	4	30	11.4	2.0	9.80	2.877140	1.360	20.600	7.21	100.60
T2	3	1	15	0	5	80	11.8	2.0	8.25	2.452133	1.150	20.600	7.17	100.60
T2	3	1	15	0	1	80	13.0	2.5	8.65	2.561812	1.280	20.600	6.76	100.60
T2	3	1	15	0	3	60	13.4	2.0	8.35	2.479553	1.350	20.600	6.19	100.60
T2	3	2	15	0	1	100	12.8	2.5	8.75	2.589232	1.160	19.990	7.54	102.60
T2	3	2	15	0	2	60	13.2	2.0	8.45	2.506973	1.240	19.990	6.81	102.60
T2	3	2	15	0	5	90	12.2	2.0	9.00	2.657782	1.240	19.990	7.26	102.60
T2	3	2	15	0	3	100	11.3	2.5	8.75	2.589232	1.360	19.990	6.43	102.60
T2	3	2	15	0	4	40	11.5	2.0	9.00	2.657782	1.290	19.990	6.98	102.60
T2	3	3	15	0	1	80	11.8	2.0	10.40	3.041659	1.200	18.370	8.67	102.14
T2	3	3	15	0	3	70	10.8	2.0	9.40	2.767461	1.300	18.370	7.23	102.14
T2	3	3	15	0	4	60	11.0	2.0	9.25	2.726331	1.360	18.370	6.80	102.14
T2	3	3	15	0	2	80	11.6	2.5	10.40	3.041659	1.320	18.370	7.88	102.14
T2	3	3	15	0	5	90	11.0	2.5	10.35	3.027949	1.340	18.370	7.72	102.14
T3	1	1	0	5	3	90	12.6	2.0	9.00	2.657782	1.420	21.100	6.34	47.60
T3	1	1	0	5	2	60	13.0	2.0	9.00	2.657782	1.320	21.100	6.82	47.60
T3	1	1	0	5	1	80	13.0	2.0	8.90	2.630362	1.400	21.100	6.36	47.60
T3	1	1	0	5	4	50	12.8	2.0	9.10	2.685201	1.150	21.100	7.91	47.60
T3	1	1	0	5	5	80	13.0	2.0	8.00	2.383584	1.420	21.100	5.63	47.60
T3	1	2	0	5	1	100	11.4	2.5	8.30	2.877140	1.380	19.280	6.01	50.88
T3	1	2	0	5	2	60	11.8	2.5	9.00	2.657782	1.390	19.280	6.47	50.88
T3	1	2	0	5	3	70	12.4	2.0	8.90	2.630362	1.160	19.280	7.67	50.88
T3	1	2	0	5	4	60	11.6	2.5	9.00	2.657782	1.280	19.280	7.03	50.88
T3	1	2	0	5	5	90	11.8	2.0	9.00	2.657782	1.290	19.280	6.98	50.88
T3	1	3	0	5	5	70	11.0	2.5	9.50	2.794881	1.240	17.620	7.66	47.80
T3	1	3	0	5	3	40	10.4	2.5	9.00	2.657782	1.360	17.620	6.62	47.80
T3	1	3	0	5	1	40	11.0	2.0	9.00	2.657782	1.320	17.620	6.82	47.80
T3	1	3	0	5	2	80	10.0	2.0	8.80	2.602942	1.340	17.620	6.57	47.80
T3	1	3	0	5	4	40	11.4	2.0	9.00	2.657782	1.250	17.620	7.20	47.80
T3	2	1	0	5	5	40	10.6	2.0	8.90	2.630362	1.350	20.352	6.59	36.00
T3	2	1	0	5	4	50	12.8	2.0	9.10	2.685201	1.290	20.352	7.05	36.00
T3	2	1	0	5	2	80	13.0	2.0	8.70	2.520683	1.200	20.352	7.25	36.00
T3	2	1	0	5	1	60	13.0	2.0	9.50	2.602942	1.280	20.352	7.42	36.00
T3	2	1	0	5	3	90	12.6	2.0	8.70	2.575522	1.270	20.352	6.85	36.00
T3	2	2	0	5	1	80	11.4	2.5	9.80	2.877140	1.340	19.576	7.31	34.44
T3	2	2	0	5	3	60	12.4	2.0	8.90	2.630362	1.250	19.576	7.12	34.44
T3	2	2	0	5	4	90	11.6	2.5	8.20	2.383584	1.390	19.576	5.90	34.44
T3	2	2	0	5	5	50	11.8	2.0	9.60	2.383584	1.350	19.576	7.11	34.44
T3	2	2	0	5	2	50	12.6	2.5	9.20	2.712621	1.280	19.576	7.19	34.44
T3	2	3	0	5	1	80	10.8	2.0	8.80	2.383584	1.240	18.276	7.10	32.88
T3	2	3	0	5	5	70	11.0	2.5	8.50	2.520683	1.340	18.276	6.34	32.88
T3	2	3	0	5	3	60	11.4	2.5	8.90	2.630362	1.290	18.276	6.90	32.88
T3	2	3	0	5	4	50	11.4	2.0	8.90	2.630362	1.350	18.276	6.59	32.88
T3	2	3	0	5	2	50	11.4	2.0	9.40	2.383584	1.280	18.276	7.34	32.88
T3	3	1	0	5	1	90	13.2	2.0	8.65	2.260000	1.320	19.734	6.55	40.48
T3	3	1	0	5	2	90	13.2	2.0	8.35	2.479553	1.280	19.734	6.52	40.48
T3	3	1	0	5	3	80	12.0	2.0	8.55	2.534393	1.350	19.734	6.33	40.48
T3	3	1	0	5	4	60	10.0	2.0	8.95	2.644072	1.360	19.734	6.58	40.48
T3	3	1	0	5	5	60	11.8	2.0	8.00	2.383584	1.420	19.734	5.63	40.48
T3	3	2	0	5	1	100	11.6	3.0	8.00	2.150000	1.400	19.234	5.71	39.40
T3	3	2	0	5	2	60	10.8	2.0	8.40	2.280000	1.250	19.234	6.72	39.40
T3	3	2	0	5	3	80	12.6	2.0	8.00	2.383584	1.260	19.234	6.35	39.40
T3	3	2	0	5	4	40	11.8	2.0	8.30	2.383584	1.290	19.234	6.43	39.40
T3	3	2	0	5	5	80	12.0	2.5	9.00	2.560000	1.350	19.234	6.67	39.40
T3	3	3	0	5	5	50	11.2	2.5	8.35	2.080000	1.350	18.688	6.19	39.92
T3	3	3	0	5	3	50	11.4	2.5	8.20	2.100000	1.260	18.688	6.51	39.92
T3	3	3	0	5	2	50	11.4	2.0	8.00	2.383584	1.250	18.688	6.40	39.92
T3	3	3	0	5	4	60	11.4	2.0	8.00	2.383584	1.380	18.688	5.80	39.92
T3	3	3	0	5	1	80	11.8	2.0	9.20	2.280000	1.370	18.688	6.72	39.92
T4	1	1	0	10	3	60	12.0	2.0	9.20	2.712621	1.240	20.440	7.42	53.40
T4	1	1	0	10	1	90	12.3	2.0	8.60	2.548103	1.220	20.440	7.05	53.40
T4	1	1	0	10	4	60	12.6	2.5	9.20	2.712621	1.210	20.440	7.60	53.40
T4	1	1	0	10	5	80	12.4	2.0	9.00	2.657782	1.290	20.440	6.98	53.40
T4	1	1	0	10	2	90	13.0	2.5	8.40	2.493263	1.230	20.440	6.83	53.40
T4	1	2	0	10	3	60	11.4	2.5	9.60	2.822300	1.140	19.598	8.42	54.70
T4	1	2	0	10	2	70	11.6	2.0	9.80	2.877140	1.250	19.598	7.84	54.70
T4	1	2	0	10	5	80	12.0	2.0	8.80	2.602942	1.350	19.598	6.52	54.70
T4	1	2	0	10	1	70	13.0	2.5	8.60	2.548103	1.280	19.598	6.72	54.70
T4	1	2	0	10	4	90	11.8	2.0	9.00	2.657782	1.110	19.598	8.11	54.70
T4	1	3	0	10	5	60	11.6	2.5	10.00	2.931980	1.120	19.508	8.93	56.60
T4	1	3	0	10	4	50	11.0	2.5	10.20	2.986819	1.140	19.508	8.95	56.60
T4	1	3	0	10	1	50	13.2	2.0	8.60	2.548103	1.340	19.508	6.42	56.60
T4	1	3	0	10	2	70	13.0	2.0	8.80	2.602942	1.150	19.508	7.65	56.60
T4	1	3	0	10	3	90	10.8	2.5	10.40	3.041659	1.160	19.508	8.97	56.60
T4	2	1	0	10	3	50	12.0	2.0	9.20	2.712621	1.250	20.880	7.36	56.48
T4	2	1	0	10	5	80	12.8	2.0	9.00	2.657782	1.320	20.880	6.82	56.48
T4	2	1	0	10	2	90	13.0	2.0	8.40	2.493263	1.230	20.880	6.83	56.48
T4	2	1	0	10	1	90	13.2	2.0	8.60	2.548103	1.220	20.880	7.05	56.48
T4	2	1	0	10	4	60	12.6	2.0	9.20	2.712621	1.290	20.880	7.13	56.48
T4	2	2	0	10	1	70	13.0	2.0	8.60	2.548103	1.110	19.578	7.75	56.60
T4	2	2	0	10	5	80	12.0	2.0	8.80	2.602942	1.150	19.578	7.65	56.60
T4	2	2	0	10	2	70	11.6	2.0	9.80	2.877140	1.120	19.578	8.75	56.60
T4	2	2	0	10	3	50	11.4	2.5	9.60	2.822300	1.230	19.578	7.80	56.60
T4	2	2	0	10	4	70	11.8	2.0	9.00	2.657782	1.140	19.578	7.89	56.60
T4	2	3	0	10	4	90	11.0	2.5	10.20	2.986819	1.340	19.508	7.61	56.46
T4	2	3	0	10	5	70	11.6	2.0	10.00	2.931980	1.360	19.508	7.35	56.46
T4	2	3	0	10	3	80	10.8	1.5	10.40	3.041659	1.320	19.508	7.88	56.46
T4	2	3	0	10	1	60	13.2	2.0	8.00	2.383584	1.180	19.508	6.78	56.46
T4	2	3	0	10	2	80	13.0	2.0	9.00	2.657782	1.290	19.508	6.98	56.46
T4	3	1	0	10	3	60	12.2	2.0	9.00	2.657782	1.380	21.084	6.52	50.40
T4	3	1	0	10	2	50	13.2	2.0	9.50	2.794881	1.360	21.084	6.99	50.40
T4	3	1	0	10	4	80	12.8	2.5	10.00	2.931980	1.390	21.084	7.19	50.40
T4	3	1	0	10	5	80	12.6	2.0	10.00	2.931980	1.240	21.084	8.06	50.40
T4	3	1	0	10	1	70	13.4	2.0	8.45	2.506973	1.340	21.084	6.31	50.40
T4	3	2	0	10	2	70	12.0	2.0	9.00	2.657782	1.150	20.318	7.83	51.00
T4	3	2	0	10	1	80	13.2	2.0	8.45	2.506973	1.340	20.318	6.31	51.00
T4	3	2	0	10	4	80	12.0	2.0	8.80	2.602942	1.180	20.318	7.46	51.00
T4	3	2	0	10	5	40	11.8	2.0	8.65	2.561812	1.350	20.318	6.41	51.00
T4	3	2	0	10	3	50	12.9	2.0	9.45	2.781171	1.420	20.318	6.65	51.00
T4	3	3	0	10	2	80	11.8	2.0	7.85	2.342454	1.390	19.336	5.65	50.20
T4	3	3	0	10	4	70	11.2	2.0	9.80	2.877140	1.410	19.336	6.95	50.20
T4	3	3	0	10	1	60	13.4	3.0	8.95	2.644072	1.360	19.336	6.58	50.20
T4	3	3	0	10	3	60	10.9	2.0	10.00	2.931980	1.380	19.336	7.25	50.20
T4	3	3	0	10	5	70	11.8	2.5	9.85	2.890850	1.250	19.336	7.88	50.20
T5	1	1	5	5	5	50	12.0	2.0	8.50	2.520683	1.280	21.018	6.64	107.40
T5	1	1	5	5	3	60	13.0	2.0	8.60	2.548103	1.340	21.018	6.42	107.40
T5	1	1	5	5	4	80	12.8	2.0	8.40	2.493263	1.220	21.018	6.89	107.40
T5	1	1	5	5	1	90	13.0	2.0	8.40	2.493263	1.280	21.018	6.56	107.40
T5	1	1	5	5	2	70	13.2	2.0	8.00	2.383584	1.350	21.018	5.93	107.40
T5	1	2	5	5	5	70	12.0	2.5	10.20	2.986819	1.280	19.168	7.97	109.04
T5	1	2	5	5	1	70	12.2	2.0	8.60	2.548103	1.290	19.168	6.67	109.04
T5	1	2	5	5	3	60	11.2	2.0	9.20	2.712621	1.400	19.168	6.57	109.04
T5	1	2	5	5	4	60	11.8	2.5	9.50	2.794881	1.300	19.168	7.31	109.04
T5	1	2	5	5	2	50	11.4	2.5	10.00	2.931980	1.369	19.168	7.30	109.04
T5	1	3	5	5	3	60	11.4	2.5	9.00	2.657782	1.361	18.756	6.61	107.48
T5	1	3	5	5	4	70	11.6	2.0	9.00	2.657782	1.150	18.756	7.83	107.48
T5	1	3	5	5	2	80	11.2	2.0	8.60	2.548103	1.270	18.756	6.77	107.48
T5	1	3	5	5	5	70	11.8	2.0	8.60	2.548103	1.180	18.756	7.29	107.48
T5	1	3	5	5	1	90	11.4	2.0	9.20	2.712621	1.290	18.756	7.13	107.48
T5	2	1	5	5	3	50	13.0	2.0	8.60	2.548103	1.350	21.018	6.37	82.60
T5	2	1	5	5	4	80	12.8	2.0	8.40	2.493263	1.360	21.018	6.18	82.60
T5	2	1	5	5	2	70	13.2	2.5	8.00	2.383584	1.320	21.018	6.06	82.60
T5	2	1	5	5	5	70	12.0	2.5	8.50	2.520683	1.280	21.018	6.64	82.60
T5	2	1	5	5	1	90	13.0	2.0	8.40	2.493263	1.300	21.018	6.46	82.60
T5	2	2	5	5	5	70	11.6	2.5	9.00	2.657782	1.340	18.996	6.72	82.28
T5	2	2	5	5	3	50	11.2	2.0	9.00	2.657782	1.250	18.996	7.20	82.28
T5	2	2	5	5	4	80	11.7	2.5	10.20	2.986819	1.380	18.996	7.39	82.28
T5	2	2	5	5	1	70	12.2	2.0	8.60	2.548103	1.290	18.996	6.67	82.28
T5	2	2	5	5	2	50	11.4	2.5	9.00	2.657782	1.260	18.996	7.14	82.28
T5	2	3	5	5	4	50	11.0	2.0	9.00	2.657782	1.160	18.362	7.76	82.00
T5	2	3	5	5	5	40	11.8	2.0	8.60	2.548103	1.150	18.362	7.48	82.00
T5	2	3	5	5	1	50	11.4	2.5	9.20	2.712621	1.390	18.362	6.62	82.00
T5	2	3	5	5	2	90	11.0	2.0	8.60	2.548103	1.410	18.362	6.10	82.00
T5	2	3	5	5	3	90	11.0	2.5	10.60	3.096499	1.320	18.362	8.03	82.00
T5	3	1	5	5	2	60	13.4	2.0	7.85	2.342454	1.320	21.359	5.95	63.56
T5	3	1	5	5	3	50	13.2	2.5	8.45	2.506973	1.350	21.359	6.26	63.56
T5	3	1	5	5	5	50	12.2	2.0	8.35	2.479553	1.340	21.359	6.23	63.56
T5	3	1	5	5	1	70	13.2	2.0	8.25	2.452133	1.300	21.359	6.35	63.56
T5	3	1	5	5	4	90	13.0	2.0	8.25	2.452133	1.360	21.359	6.07	63.56
T5	3	2	5	5	2	60	11.6	2.0	9.85	2.890850	1.250	18.824	7.88	61.40
T5	3	2	5	5	4	90	11.2	2.0	10.10	2.959399	1.220	18.824	8.28	61.40
T5	3	2	5	5	5	60	11.0	2.0	10.10	2.959399	1.210	18.824	8.35	61.40
T5	3	2	5	5	1	80	12.4	2.0	8.45	2.506973	1.280	18.824	6.60	61.40
T5	3	2	5	5	3	60	11.4	2.0	9.10	2.685201	1.270	18.824	7.17	61.40
T5	3	3	5	5	4	60	11.8	2.0	8.85	2.616652	1.350	18.516	6.56	63.66
T5	3	3	5	5	5	90	12.0	2.0	8.45	2.506973	1.360	18.516	6.21	63.66
T5	3	3	5	5	2	70	10.5	2.0	8.45	2.506973	1.350	18.516	6.26	63.66
T5	3	3	5	5	1	80	11.6	1.5	9.10	2.685201	1.310	18.516	6.95	63.66
T5	3	3	5	5	3	70	10.8	2.5	10.00	2.931980	1.380	18.516	7.25	63.66
T6	1	1	5	10	2	90	11.4	2.0	8.60	2.548103	1.290	19.508	6.67	74.50
T6	1	1	5	10	3	90	12.4	2.0	9.00	2.657782	1.320	19.508	6.82	74.50
T6	1	1	5	10	4	50	11.8	2.0	9.20	2.712621	1.310	19.508	7.02	74.50
T6	1	1	5	10	1	80	11.6	2.0	8.80	2.602942	1.280	19.508	6.88	74.50
T6	1	1	5	10	5	80	12.4	2.0	8.40	2.493263	1.250	19.508	6.72	74.50
T6	1	2	5	10	2	90	13.2	2.0	9.60	2.822300	1.340	20.640	7.16	76.60
T6	1	2	5	10	1	90	12.8	2.0	8.80	2.602942	1.360	20.640	6.47	76.60
T6	1	2	5	10	5	40	13.2	2.0	8.30	2.465843	1.260	20.640	6.59	76.60
T6	1	2	5	10	3	80	10.9	2.0	9.50	2.794881	1.410	20.640	6.74	76.60
T6	1	2	5	10	4	80	12.8	2.0	9.40	2.767461	1.250	20.640	7.52	76.60
T6	1	3	5	10	5	60	10.8	2.5	10.20	2.986819	1.220	19.188	8.36	71.90
T6	1	3	5	10	4	80	11.4	2.5	9.60	2.822300	1.240	19.188	7.74	71.90
T6	1	3	5	10	1	50	12.8	2.0	10.00	2.931980	1.310	19.188	7.63	71.90
T6	1	3	5	10	2	90	12.2	2.0	9.90	2.904560	1.240	19.188	7.98	71.90
T6	1	3	5	10	3	60	11.4	2.5	9.90	2.904560	1.150	19.188	8.61	71.90
T6	2	1	5	10	1	50	11.6	2.0	8.80	2.602942	1.220	19.392	7.21	123.40
T6	2	1	5	10	4	90	11.8	2.0	9.20	2.712621	1.220	19.392	7.54	123.40
T6	2	1	5	10	2	60	11.4	2.0	8.60	2.548103	1.310	19.392	6.56	123.40
T6	2	1	5	10	3	70	12.4	2.0	9.00	2.657782	1.240	19.392	7.26	123.40
T6	2	1	5	10	5	50	12.0	2.0	8.40	2.493263	1.190	19.392	7.06	123.40
T6	2	2	5	10	1	90	11.6	2.0	8.80	2.602942	1.170	21.114	7.52	120.60
T6	2	2	5	10	2	70	13.2	2.0	9.60	2.822300	1.150	21.114	8.35	120.60
T6	2	2	5	10	3	80	13.4	2.0	9.50	2.794881	1.260	21.114	7.54	120.60
T6	2	2	5	10	4	80	12.8	2.0	8.70	2.575522	1.250	21.114	6.96	120.60
T6	2	2	5	10	5	90	13.3	2.0	9.60	2.822300	1.290	21.114	7.44	120.60
T6	2	3	5	10	4	90	11.8	2.5	9.50	2.794881	1.280	19.576	7.42	120.68
T6	2	3	5	10	2	80	12.2	2.0	9.60	2.822300	1.340	19.576	7.16	120.68
T6	2	3	5	10	5	70	10.8	2.5	10.20	2.986819	1.250	19.576	8.16	120.68
T6	2	3	5	10	1	60	12.4	2.0	10.00	2.931980	1.350	19.576	7.41	120.68
T6	2	3	5	10	3	40	12.6	2.5	9.80	2.877140	1.260	19.576	7.78	120.68
T6	3	1	5	10	4	50	12.0	2.5	9.10	2.685201	1.350	19.714	6.74	72.76
T6	3	1	5	10	3	90	12.6	2.0	8.85	2.616652	1.260	19.714	7.02	72.76
T6	3	1	5	10	1	80	11.8	2.0	8.65	2.561812	1.280	19.714	6.76	72.76
T6	3	1	5	10	5	60	12.2	2.0	9.00	2.657782	1.340	19.714	6.72	72.76
T6	3	1	5	10	2	90	11.6	2.5	8.45	2.506973	1.290	19.714	6.55	72.76
T6	3	2	5	10	2	90	13.4	2.0	9.45	2.781171	1.310	21.222	7.21	75.60
T6	3	2	5	10	1	50	11.8	2.0	8.65	2.561812	1.320	21.222	6.55	75.60
T6	3	2	5	10	4	80	13.0	2.0	9.00	2.657782	1.290	21.222	6.98	75.60
T6	3	2	5	10	5	90	12.8	2.0	9.00	2.657782	1.420	21.222	6.34	75.60
T6	3	2	5	10	3	80	13.6	2.5	9.35	2.753751	1.280	21.222	7.30	75.60
T6	3	3	5	10	5	80	11.0	2.5	10.10	2.959399	1.280	19.666	7.89	73.40
T6	3	3	5	10	3	90	11.2	2.0	10.00	2.931980	1.220	19.666	8.20	73.40
T6	3	3	5	10	2	80	12.4	2.0	10.00	2.931980	1.160	19.666	8.62	73.40
T6	3	3	5	10	4	40	12.8	2.0	10.00	2.931980	1.240	19.666	8.06	73.40
T6	3	3	5	10	1	60	12.6	2.0	10.00	2.931980	1.140	19.666	8.77	73.40
T7	1	1	15	5	3	80	11.8	2.5	10.40	3.041659	1.290	18.822	8.06	110.00
T7	1	1	15	5	1	90	11.6	2.0	9.80	2.877140	1.220	18.822	8.03	110.00
T7	1	1	15	5	2	80	11.4	2.0	9.60	2.822300	1.240	18.822	7.74	110.00
T7	1	1	15	5	4	60	11.0	2.0	9.60	2.822300	1.180	18.822	8.14	110.00
T7	1	1	15	5	5	90	11.8	2.0	9.80	2.877140	1.150	18.822	8.52	110.00
T7	1	2	15	5	1	50	10.8	2.5	9.80	2.877140	1.140	20.538	8.60	109.40
T7	1	2	15	5	5	80	12.8	2.0	9.00	2.657782	1.340	20.538	6.72	109.40
T7	1	2	15	5	2	80	13.0	2.0	8.80	2.602942	1.160	20.538	7.59	109.40
T7	1	2	15	5	3	80	13.2	2.0	8.00	2.383584	1.170	20.538	6.84	109.40
T7	1	2	15	5	4	90	12.8	2.5	9.00	2.657782	1.320	20.538	6.82	109.40
T7	1	3	15	5	1	80	12.8	2.0	9.00	2.657782	1.350	21.088	6.67	106.20
T7	1	3	15	5	4	80	13.0	2.0	8.20	2.438423	1.320	21.088	6.21	106.20
T7	1	3	15	5	5	70	12.8	2.0	10.00	2.931980	1.360	21.088	7.35	106.20
T7	1	3	15	5	2	50	12.8	2.5	9.00	2.657782	1.360	21.088	6.62	106.20
T7	1	3	15	5	3	70	12.8	2.0	8.00	2.383584	1.280	21.088	6.25	106.20
T7	2	1	15	5	4	40	12.8	2.0	9.00	2.657782	1.280	20.770	7.03	104.20
T7	2	1	15	5	3	80	12.8	2.5	10.40	3.041659	1.310	20.770	7.94	104.20
T7	2	1	15	5	1	90	12.8	2.0	9.80	2.877140	1.280	20.770	7.66	104.20
T7	2	1	15	5	2	80	12.8	2.0	9.60	2.822300	1.290	20.770	7.44	104.20
T7	2	1	15	5	5	90	11.8	2.0	9.20	2.712621	1.190	20.770	7.73	104.20
T7	2	2	15	5	4	90	11.2	2.5	10.00	2.931980	1.240	19.600	8.06	105.00
T7	2	2	15	5	5	80	12.2	2.0	9.00	2.657782	1.250	19.600	7.20	105.00
T7	2	2	15	5	1	70	10.8	2.5	9.80	2.877140	1.240	19.600	7.90	105.00
T7	2	2	15	5	3	50	13.2	2.0	8.60	2.548103	1.310	19.600	6.56	105.00
T7	2	2	15	5	2	60	12.4	2.0	8.80	2.602942	1.220	19.600	7.21	105.00
T7	2	3	15	5	5	50	13.4	2.0	9.00	2.657782	1.350	19.596	6.67	104.40
T7	2	3	15	5	2	50	10.8	2.5	9.50	2.794881	1.240	19.596	7.66	104.40
T7	2	3	15	5	4	60	12.0	2.0	9.00	2.657782	1.290	19.596	6.98	104.40
T7	2	3	15	5	1	80	12.0	2.0	9.60	2.822300	1.260	19.596	7.62	104.40
T7	2	3	15	5	3	90	12.0	2.0	9.00	2.657782	1.280	19.596	7.03	104.40
T7	3	1	15	5	3	90	12.8	3.0	10.25	2.260000	1.250	20.802	8.20	107.20
T7	3	1	15	5	1	80	12.9	2.0	9.65	2.836010	1.180	20.802	8.18	107.20
T7	3	1	15	5	2	90	12.8	3.0	9.45	2.781171	1.190	20.802	7.94	107.20
T7	3	1	15	5	4	80	12.9	2.0	8.85	2.616652	1.240	20.802	7.14	107.20
T7	3	1	15	5	5	50	12.0	2.0	9.10	2.685201	1.260	20.802	7.22	107.20
T7	3	2	15	5	3	80	13.0	2.0	8.45	2.506973	1.340	20.752	6.31	108.40
T7	3	2	15	5	1	70	12.8	2.0	9.65	2.340000	1.220	20.752	7.91	108.40
T7	3	2	15	5	2	60	11.0	2.0	8.65	2.561812	1.230	20.752	7.03	108.40
T7	3	2	15	5	4	90	13.4	2.5	9.85	2.890850	1.335	20.752	7.38	108.40
T7	3	2	15	5	5	80	13.0	2.0	9.10	2.685201	1.360	20.752	6.69	108.40
T7	3	3	15	5	4	80	12.0	2.0	9.00	2.657782	1.300	19.976	6.92	109.40
T7	3	3	15	5	5	60	12.0	2.0	9.85	2.890850	1.200	19.976	8.21	109.40
T7	3	3	15	5	1	90	11.4	2.5	9.45	2.781171	1.390	19.976	6.80	109.40
T7	3	3	15	5	2	50	12.9	2.0	9.35	2.753751	1.250	19.976	7.48	109.40
T7	3	3	15	5	3	80	12.6	2.0	9.25	2.726331	1.220	19.976	7.58	109.40
T8	1	1	15	10	3	90	13.9	2.5	9.00	2.657782	1.200	21.564	7.50	119.48
T8	1	1	15	10	4	80	13.3	2.0	9.40	2.767461	1.230	21.564	7.64	119.48
T8	1	1	15	10	2	90	13.8	2.5	9.40	2.657782	1.240	21.564	7.58	119.48
T8	1	1	15	10	5	50	13.8	2.5	8.50	2.602942	1.140	21.564	7.46	119.48
T8	1	1	15	10	1	60	10.8	2.5	9.20	3.069079	1.280	21.564	7.19	119.48
T8	1	2	15	10	3	50	12.6	2.5	9.50	2.986819	1.320	21.324	7.20	118.30
T8	1	2	15	10	1	60	13.2	2.5	8.30	2.465843	1.130	21.324	7.35	118.30
T8	1	2	15	10	4	90	11.6	2.0	9.90	2.904560	1.190	21.324	8.32	118.30
T8	1	2	15	10	5	90	14.0	2.5	9.00	2.657782	1.180	21.324	7.63	118.30
T8	1	2	15	10	2	90	13.5	2.5	9.90	2.904560	1.310	21.324	7.56	118.30
T8	1	3	15	10	1	80	13.4	2.5	8.00	2.383584	1.220	21.720	6.56	120.10
T8	1	3	15	10	5	90	12.6	2.5	8.20	2.383584	1.280	21.720	6.41	120.10
T8	1	3	15	10	4	80	13.6	2.5	8.00	2.383584	1.260	21.720	6.35	120.10
T8	1	3	15	10	2	70	13.0	2.0	9.00	2.657782	1.240	21.720	7.26	120.10
T8	1	3	15	10	3	90	13.4	2.0	8.20	2.383584	1.250	21.720	6.56	120.10
T8	2	1	15	10	2	90	12.8	2.5	9.80	2.877140	1.150	20.886	8.52	126.48
T8	2	1	15	10	1	70	12.8	2.5	10.50	3.069079	1.220	20.886	8.61	126.48
T8	2	1	15	10	3	90	11.8	2.5	9.70	2.849720	1.160	20.886	8.36	126.48
T8	2	1	15	10	4	90	13.3	3.0	9.40	2.767461	1.140	20.886	8.25	126.48
T8	2	1	15	10	5	70	12.9	2.5	8.80	2.602942	1.180	20.886	7.46	126.48
T8	2	2	15	10	1	50	13.2	2.5	10.30	3.014239	1.290	21.220	7.98	128.20
T8	2	2	15	10	2	90	12.4	2.5	9.00	2.657782	1.240	21.220	7.26	128.20
T8	2	2	15	10	3	80	12.4	2.5	10.20	2.986819	1.220	21.220	8.36	128.20
T8	2	2	15	10	4	60	12.6	2.5	9.30	2.740041	1.230	21.220	7.56	128.20
T8	2	2	15	10	5	80	14.0	2.5	9.00	2.657782	1.150	21.220	7.83	128.20
T8	2	3	15	10	5	90	12.9	2.5	8.00	2.383584	1.120	21.394	7.14	126.80
T8	2	3	15	10	2	80	13.0	2.0	8.20	2.438423	1.140	21.394	7.19	126.80
T8	2	3	15	10	3	90	13.4	2.0	8.00	2.383584	1.130	21.394	7.08	126.80
T8	2	3	15	10	4	70	13.6	2.5	9.00	2.657782	1.110	21.394	8.11	126.80
T8	2	3	15	10	1	80	12.2	2.5	12.10	3.507795	1.160	21.394	10.43	126.80
T8	3	1	15	10	1	70	12.0	3.0	9.90	3.027949	1.380	19.600	7.17	94.20
T8	3	1	15	10	2	80	12.2	2.5	10.20	2.836010	1.170	19.600	8.72	94.20
T8	3	1	15	10	3	70	12.8	3.0	9.70	2.808591	1.180	19.600	8.22	94.20
T8	3	1	15	10	4	80	13.5	2.5	9.25	2.830000	1.350	19.600	6.85	94.20
T8	3	1	15	10	5	90	10.8	3.0	9.80	2.920000	1.280	19.600	7.66	94.20
T8	3	2	15	10	1	90	13.4	2.5	9.70	2.973109	1.390	20.804	6.98	94.20
T8	3	2	15	10	2	80	12.9	2.5	9.80	2.940000	1.180	20.804	8.31	94.20
T8	3	2	15	10	3	70	12.9	3.0	9.60	2.580000	1.250	20.804	7.68	94.20
T8	3	2	15	10	4	70	12.9	2.0	9.80	3.010000	1.160	20.804	8.45	94.20
T8	3	2	15	10	5	60	14.2	2.5	9.80	2.860000	1.350	20.804	7.26	94.20
T8	3	3	15	10	1	90	12.4	2.5	9.00	2.880000	1.360	20.140	6.62	94.30
T8	3	3	15	10	2	80	13.2	2.0	8.70	2.630362	1.340	20.140	6.49	94.30
T8	3	3	15	10	3	90	13.6	3.0	10.20	2.880000	1.380	20.140	7.39	94.30
T8	3	3	15	10	4	90	13.8	2.5	9.20	2.726331	1.340	20.140	6.87	94.30
T8	3	3	15	10	5	90	12.8	3.0	9.40	2.950000	1.360	20.140	6.91	94.30

consumo %>% kable(caption = "Evaluation of fruits at commercial maturity")

Evaluation of fruits at commercial maturity

treat	composts	biol	repetition	n_fruits	pdfmc	ffmc	cifmc	ssfmc	phfmc	atfmc	imf
T0	0	0	1	1	6.68	3.0	3.0	14.6	4.22	0.600	24.33333
T0	0	0	1	2	6.78	3.0	3.0	15.6	4.19	0.700	22.28571
T0	0	0	1	3	7.02	4.0	3.0	14.8	4.21	0.700	21.14286
T0	0	0	1	4	6.78	3.8	3.0	15.0	4.15	0.500	30.00000
T0	0	0	1	5	6.12	4.2	3.0	14.0	4.26	0.500	28.00000
T0	0	0	2	1	6.62	4.0	3.0	14.4	4.34	0.605	23.80165
T0	0	0	2	2	6.91	3.6	3.5	15.5	4.35	0.615	25.20325
T0	0	0	2	3	7.04	3.0	3.0	15.1	4.34	0.625	24.16000
T0	0	0	2	4	6.45	3.0	3.0	15.2	4.30	0.600	25.33333
T0	0	0	2	5	6.50	3.0	3.0	15.2	4.38	0.600	25.33333
T0	0	0	3	1	6.55	3.0	3.5	15.0	4.45	0.595	25.21008
T0	0	0	3	2	6.85	3.4	3.0	15.0	4.46	0.590	25.42373
T0	0	0	3	3	7.12	4.2	3.0	14.5	4.50	0.590	24.57627
T0	0	0	3	4	6.95	3.8	3.5	15.0	4.42	0.600	25.00000
T0	0	0	3	5	7.04	4.2	2.5	15.0	4.41	0.600	25.00000
T1	5	0	1	1	6.62	4.2	3.5	14.0	4.10	0.710	19.71831
T1	5	0	1	2	6.70	3.8	3.0	14.0	4.00	0.720	19.44444
T1	5	0	1	3	6.47	3.8	4.0	14.0	4.20	0.740	18.91892
T1	5	0	1	4	6.53	3.8	3.5	15.6	4.30	0.690	22.60870
T1	5	0	1	5	6.60	3.6	4.0	15.0	3.90	0.710	21.12676
T1	5	0	2	1	6.45	3.8	3.0	14.0	4.42	0.620	22.58065
T1	5	0	2	2	6.87	3.6	3.0	14.0	4.45	0.600	23.33333
T1	5	0	2	3	6.48	3.4	3.0	15.6	4.41	0.640	24.37500
T1	5	0	2	4	6.70	4.0	3.5	15.5	4.43	0.630	24.60317
T1	5	0	2	5	6.35	4.2	3.5	15.0	4.41	0.620	24.19355
T1	5	0	3	1	6.95	4.0	3.5	14.8	4.00	0.520	28.46154
T1	5	0	3	2	6.62	4.4	4.0	16.2	3.80	0.500	32.40000
T1	5	0	3	3	6.70	3.8	4.0	14.6	3.90	0.530	27.54717
T1	5	0	3	4	7.04	3.0	4.2	16.4	4.20	0.540	30.37037
T1	5	0	3	5	7.20	3.8	4.2	14.0	4.10	0.520	26.92308
T2	15	0	1	1	6.07	4.8	3.5	16.0	4.55	0.520	30.76923
T2	15	0	1	2	6.20	4.0	4.0	15.8	4.50	0.510	30.98039
T2	15	0	1	3	6.28	4.2	3.0	15.8	4.56	0.520	30.38462
T2	15	0	1	4	6.37	3.8	3.0	15.8	4.54	0.530	29.81132
T2	15	0	1	5	6.70	3.8	4.0	14.6	4.58	0.540	27.03704
T2	15	0	2	1	5.95	3.8	4.5	16.2	4.50	0.515	31.45631
T2	15	0	2	2	6.20	4.0	3.5	15.0	4.44	0.510	29.41176
T2	15	0	2	3	6.11	4.0	3.0	15.2	4.48	0.535	28.41121
T2	15	0	2	4	6.28	3.8	4.5	15.8	4.56	0.505	31.28713
T2	15	0	2	5	6.87	4.8	4.0	16.0	4.50	0.515	31.06796
T2	15	0	3	1	6.20	4.2	3.5	14.8	4.52	0.520	28.46154
T2	15	0	3	2	6.28	4.0	4.0	15.4	4.45	0.525	29.33333
T2	15	0	3	3	6.70	4.0	4.0	15.8	4.57	0.510	30.98039
T2	15	0	3	4	6.20	4.2	3.0	16.0	4.49	0.515	31.06796
T2	15	0	3	5	6.11	4.4	3.5	16.0	4.56	0.520	30.76923
T3	0	5	1	1	6.70	3.2	3.5	14.5	4.20	0.585	24.78632
T3	0	5	1	2	6.62	3.4	4.0	14.8	4.15	0.595	24.87395
T3	0	5	1	3	6.62	3.0	3.5	15.0	4.25	0.595	25.21008
T3	0	5	1	4	6.62	3.8	3.5	15.2	4.18	0.585	25.98291
T3	0	5	1	5	6.11	4.2	3.0	16.0	4.22	0.590	27.11864
T3	0	5	2	1	6.78	4.4	3.0	14.2	4.51	0.595	23.86555
T3	0	5	2	2	6.28	3.2	4.0	14.8	4.48	0.605	24.46281
T3	0	5	2	3	6.37	3.0	3.5	14.6	4.46	0.595	24.53782
T3	0	5	2	4	6.62	3.0	3.5	15.0	4.58	0.595	25.21008
T3	0	5	2	5	6.70	3.0	3.5	16.4	4.54	0.605	27.10744
T3	0	5	3	1	6.20	3.0	4.0	14.8	4.39	0.590	25.08475
T3	0	5	3	2	6.45	3.6	3.5	15.0	4.40	0.595	25.21008
T3	0	5	3	3	6.62	3.0	3.5	16.0	4.36	0.585	27.35043
T3	0	5	3	4	6.70	4.0	4.0	14.8	4.45	0.615	24.06504
T3	0	5	3	5	6.70	3.8	3.5	14.6	4.37	0.580	25.17241
T4	0	10	1	1	6.37	4.2	4.0	15.2	4.38	0.550	27.63636
T4	0	10	1	2	6.45	4.2	4.0	15.4	4.36	0.620	24.83871
T4	0	10	1	3	6.45	4.0	3.5	15.4	4.35	0.615	25.04065
T4	0	10	1	4	6.40	4.0	4.0	15.4	4.42	0.625	24.64000
T4	0	10	1	5	6.41	4.0	4.0	15.3	4.40	0.555	27.56757
T4	0	10	2	1	6.45	4.0	4.0	15.4	4.39	0.545	28.25688
T4	0	10	2	2	6.41	3.8	3.5	16.3	4.41	0.615	26.50407
T4	0	10	2	3	6.62	3.6	4.0	15.8	4.42	0.625	25.28000
T4	0	10	2	4	6.20	4.2	3.5	14.8	4.37	0.605	24.46281
T4	0	10	2	5	6.32	4.8	4.0	15.1	4.38	0.615	24.55285
T4	0	10	3	1	6.24	4.2	4.0	14.9	4.39	0.580	25.68966
T4	0	10	3	2	6.11	4.0	2.5	15.6	4.43	0.595	26.21849
T4	0	10	3	3	6.62	4.2	3.5	15.8	4.35	0.570	27.71930
T4	0	10	3	4	6.62	4.0	4.0	15.8	4.36	0.600	26.33333
T4	0	10	3	5	6.45	3.6	4.0	15.4	4.41	0.605	25.45455
T5	5	5	1	1	6.53	4.0	4.0	14.8	4.25	0.590	25.08475
T5	5	5	1	2	6.62	4.2	4.0	16.0	4.55	0.545	29.35780
T5	5	5	1	3	6.45	4.0	3.0	14.3	4.60	0.535	26.72897
T5	5	5	1	4	6.53	4.0	3.5	15.0	4.44	0.555	27.02703
T5	5	5	1	5	6.28	3.8	3.5	16.0	4.26	0.540	29.62963
T5	5	5	2	1	6.70	4.0	3.5	14.9	4.41	0.595	25.04202
T5	5	5	2	2	6.70	4.0	3.5	16.0	4.45	0.535	29.90654
T5	5	5	2	3	6.45	4.0	3.5	15.2	4.38	0.545	27.88991
T5	5	5	2	4	6.45	4.0	3.5	15.3	4.46	0.555	27.56757
T5	5	5	2	5	6.20	4.2	4.0	14.8	4.45	0.540	27.40741
T5	5	5	3	1	6.41	4.0	4.0	15.4	4.39	0.545	28.25688
T5	5	5	3	2	6.37	4.0	4.0	15.2	4.45	0.555	27.38739
T5	5	5	3	3	6.62	4.0	3.5	15.2	4.39	0.535	28.41121
T5	5	5	3	4	6.70	4.2	3.5	15.2	4.47	0.540	28.14815
T5	5	5	3	5	6.37	4.2	3.5	15.0	4.49	0.535	28.03738
T6	5	10	1	1	6.20	4.8	3.5	15.6	4.48	0.535	29.15888
T6	5	10	1	2	6.70	4.0	4.0	15.8	4.46	0.525	30.09524
T6	5	10	1	3	5.99	4.2	3.5	16.4	4.51	0.545	30.09174
T6	5	10	1	4	6.28	3.8	3.5	15.6	4.52	0.525	29.71429
T6	5	10	1	5	6.70	3.6	3.5	15.0	4.45	0.525	28.57143
T6	5	10	2	1	6.24	3.8	3.0	16.0	4.42	0.535	29.90654
T6	5	10	2	2	6.70	4.0	4.0	16.0	4.25	0.565	28.31858
T6	5	10	2	3	6.37	4.0	3.5	15.4	4.21	0.535	28.78505
T6	5	10	2	4	6.41	3.8	4.0	15.4	4.28	0.555	27.74775
T6	5	10	2	5	6.20	4.8	3.5	14.8	4.29	0.525	28.19048
T6	5	10	3	1	6.45	4.2	4.0	14.4	4.68	0.530	27.16981
T6	5	10	3	2	6.37	4.0	3.5	15.2	4.75	0.535	28.41121
T6	5	10	3	3	6.37	4.0	3.5	15.2	4.61	0.525	28.95238
T6	5	10	3	4	6.37	4.2	4.0	16.0	4.73	0.540	29.62963
T6	5	10	3	5	6.28	4.2	3.5	15.2	4.62	0.520	29.23077
T7	15	5	1	1	6.11	4.8	3.5	16.2	4.57	0.515	31.45631
T7	15	5	1	2	6.53	4.0	3.0	16.2	4.51	0.520	31.15385
T7	15	5	1	3	6.20	4.0	3.0	16.8	4.52	0.510	32.94118
T7	15	5	1	4	6.28	4.2	4.0	14.8	4.59	0.515	28.73786
T7	15	5	1	5	5.86	4.0	3.5	14.6	4.64	0.520	28.07692
T7	15	5	2	1	6.03	4.2	3.5	15.8	4.56	0.510	30.98039
T7	15	5	2	2	6.28	3.8	4.0	15.9	4.54	0.515	30.87379
T7	15	5	2	3	6.32	4.2	4.0	16.8	4.58	0.505	33.26733
T7	15	5	2	4	6.37	4.2	4.0	15.4	4.49	0.505	30.49505
T7	15	5	2	5	6.37	4.8	4.0	14.6	4.62	0.505	28.91089
T7	15	5	3	1	6.28	4.4	3.5	15.2	4.58	0.520	29.23077
T7	15	5	3	2	6.28	4.4	4.0	15.2	4.57	0.528	28.78788
T7	15	5	3	3	6.07	4.6	4.0	16.2	4.56	0.505	32.07921
T7	15	5	3	4	6.28	3.6	3.5	15.4	4.61	0.515	29.90291
T7	15	5	3	5	6.28	4.0	4.0	16.8	4.59	0.520	32.30769
T8	15	10	1	1	5.86	4.0	4.0	15.8	4.60	0.520	30.38462
T8	15	10	1	2	5.86	4.0	4.0	16.0	4.52	0.530	30.18868
T8	15	10	1	3	5.86	4.0	4.0	16.4	4.58	0.540	30.37037
T8	15	10	1	4	6.13	4.2	3.5	15.6	4.63	0.520	30.00000
T8	15	10	1	5	6.18	4.6	3.0	16.0	4.65	0.500	32.00000
T8	15	10	2	1	5.86	4.4	4.0	15.4	4.67	0.510	30.19608
T8	15	10	2	2	5.96	5.2	4.0	16.4	4.71	0.470	34.89362
T8	15	10	2	3	6.53	4.0	3.5	16.2	4.65	0.490	33.06122
T8	15	10	2	4	6.39	4.2	4.0	16.0	4.63	0.500	32.00000
T8	15	10	2	5	6.32	4.0	3.5	16.0	4.69	0.490	32.65306
T8	15	10	3	1	6.20	4.4	4.0	16.6	4.81	0.410	40.48780
T8	15	10	3	2	6.78	4.2	4.0	15.5	4.72	0.400	38.75000
T8	15	10	3	3	5.95	4.4	4.0	16.0	4.87	0.420	38.09524
T8	15	10	3	4	6.70	4.8	3.5	16.7	4.85	0.390	42.82051
T8	15	10	3	5	5.86	4.6	4.0	17.2	4.81	0.430	40.00000

3 Data summary

Summary of the number of data points recorded for each treatment and evaluated variable.

sm <- fisio %>% 
  group_by(treat) %>% 
  summarise(across(pcfmf:rpp, ~ sum(!is.na(.))))

sm
## # A tibble: 9 × 10
##   treat pcfmf  ffmf cifmf ssfmf phfmf atfmf msfmf   imf   rpp
##   <fct> <int> <int> <int> <int> <int> <int> <int> <int> <int>
## 1 T0       45    45    45    45    45    45    45    45    45
## 2 T1       45    45    45    45    45    45    45    45    45
## 3 T2       45    45    45    45    45    45    45    45    45
## 4 T3       45    45    45    45    45    45    45    45    45
## 5 T4       45    45    45    45    45    45    45    45    45
## 6 T5       45    45    45    45    45    45    45    45    45
## 7 T6       45    45    45    45    45    45    45    45    45
## 8 T7       45    45    45    45    45    45    45    45    45
## 9 T8       45    45    45    45    45    45    45    45    45

sm <- consumo %>% 
  group_by(treat) %>% 
  summarise(across(pdfmc:imf, ~ sum(!is.na(.))))

sm
## # A tibble: 9 × 8
##   treat pdfmc  ffmc cifmc ssfmc phfmc atfmc   imf
##   <fct> <int> <int> <int> <int> <int> <int> <int>
## 1 T0       15    15    15    15    15    15    15
## 2 T1       15    15    15    15    15    15    15
## 3 T2       15    15    15    15    15    15    15
## 4 T3       15    15    15    15    15    15    15
## 5 T4       15    15    15    15    15    15    15
## 6 T5       15    15    15    15    15    15    15
## 7 T6       15    15    15    15    15    15    15
## 8 T7       15    15    15    15    15    15    15
## 9 T8       15    15    15    15    15    15    15

4 Meteorological data

Climatic conditions of the study area located in the Tambogrande district, Piura region.

met <- range_read(ss = gs, sheet = "clima") %>% 
  mutate(date = as_date(Fecha))

scale <- 3

plot <- met %>% 
  ggplot(aes(x = date)) +
  geom_line(aes(y = TMax, color = "Tmax (°C)"), size= 0.8) + 
  geom_line(aes(y = TMin, color = "Tmin (°C)"), size= 0.8) +
  geom_bar(aes(y = PP/scale)
            , stat="identity", size=.1, fill="blue", color="black", alpha=.4) +
  geom_line(aes(y = HR/scale, color = "HR (%)"), size = 0.8) +
  scale_color_manual("", values = c("skyblue", "red", "blue")) +
  scale_y_continuous(limits = c(0, 40)
                     , expand = c(0, 0)
                     , name = "Temperature (°C)"
                     , sec.axis = sec_axis(~ . * scale, name = "Precipitation (mm)")
                     ) +
  scale_x_date(date_breaks = "1 month", date_labels = "%m-%Y", name = NULL) +
  theme_minimal_grid() +
  theme(legend.position = "top")

plot %>% 
  ggsave2(plot = ., "submission/Figure_2.jpg", units = "cm"
         , width = 25, height = 15)

plot %>% 
  ggsave2(plot = ., "submission/Figure_2.eps", units = "cm"
         , width = 25, height = 15)

knitr::include_graphics("submission/Figure_2.jpg")

5 Objetives

The objective of this study is to demonstrate the effect of organic fertilizers, specifically compost and biol, applied at the soil and foliar levels on the quality of mango fruit at physiological and commercial maturity.

5.1 Specific Objective 1

Demonstrate the effect of organic fertilizers, specifically compost and biol, applied at the soil and foliar levels on the quality of mango fruit at physiological maturity.

5.1.1 Fruit firmness at physiological maturity (FF_pm)

trait <- "ffmf"

fb <- fisio

lmm <- paste({{trait}}, "~ 1 + (1|repetition) + composts*biol") %>% as.formula()

rmout <- fb %>% 
  remove_outliers(formula = lmm
                  , drop_na = T, plot_diag = T)

plot_diagnostic(rmout$data$clean, formula = lmm) %>% 
  plot_grid(plotlist = ., ncol = 2)

rmout$outliers
##  [1] index       repetition  composts    biol        ffmf        resi       
##  [7] res_MAD     rawp.BHStud adjp        bholm       out_flag   
## <0 rows> (o 0- extensión row.names)

model <- rmout$data$clean %>% 
  lmer(formula = lmm, .)

Anova(model, type = 3, test.statistic = "F")
## Analysis of Deviance Table (Type III Wald F tests with Kenward-Roger df)
## 
## Response: ffmf
##                       F Df Df.res                Pr(>F)    
## (Intercept)   5343.0467  1  12.86 < 0.00000000000000022 ***
## composts         5.5087  2 394.00              0.004369 ** 
## biol            10.4587  2 394.00            0.00003753 ***
## composts:biol    0.5815  4 394.00              0.676230    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

mc1 <- emmeans(model, ~ biol|composts) %>%
  cld(Letters = letters, reversed = T) %>% 
  mutate(across(".group", ~ trimws(.))) %>% 
  rename(sig1 = ".group")

mc2 <- emmeans(model, ~ composts|biol) %>%
  cld(Letters = letters, reversed = T) %>% 
  mutate(across(".group", ~ trimws(.))) %>% 
  mutate(across(".group", ~ toupper(.))) %>% 
  rename(sig2 = ".group")

mc <- merge(mc1, mc2) %>% 
  unite(col = "group", c("sig1", "sig2"), sep = "")

mc %>% kable()

biol	composts	emmean	SE	df	lower.CL	upper.CL	group
0	0	11.38667	0.1557766	12.86214	11.04976	11.72357	bB
0	15	11.95556	0.1557766	12.86214	11.61865	12.29246	bA
0	5	11.49333	0.1557766	12.86214	11.15643	11.83024	bB
10	0	12.22000	0.1557766	12.86214	11.88310	12.55690	aB
10	15	12.96000	0.1557766	12.86214	12.62310	13.29690	aA
10	5	12.20444	0.1557766	12.86214	11.86754	12.54135	aB
5	0	11.80444	0.1557766	12.86214	11.46754	12.14135	abB
5	15	12.33111	0.1557766	12.86214	11.99421	12.66801	bA
5	5	11.94667	0.1557766	12.86214	11.60976	12.28357	aAB

p1a <- mc %>%
  plot_smr(x = "composts"
           , y = "emmean"
           , group = "biol"
           , sig = "group"
           , error = "SE"
           , color = T
           , xlab = "Composts"
           , ylab = "Fruit firmness at physiological maturity (kgf/cm^{2})"
           , glab = "Biol"
           # , ylimits = c(0, 14, 4)
           , type = "line"
           )

p1a

mc_1a <- emmeans(model, ~ biol*composts) %>%
  cld(Letters = letters, reversed = T) %>% 
  mutate(across(".group", ~ trimws(.)))

p1_a <- mc_1a %>%
  plot_smr(x = "composts"
           , y = "emmean"
           , group = "biol"
           , sig = ".group"
           , error = "SE"
           , color = T
           , xlab = "Composts"
           , ylab = "Fruit firmness at physiological maturity (kgf/cm^{2})"
           , glab = "Biol"
           , ylimits = c(0, 15, 3)
           , type = "bar"
           )


# # Calcular el valor máximo (upper whisker) por grupo
# positions <-  rmout$data$clean %>%
#   group_by(composts, biol) %>%
#   summarise(y_pos = max(ffmf, na.rm = TRUE) + 0.3, .groups = "drop")
# 
# # Combinar con las letras del objeto mc
# mc_pos <- left_join(mc, positions, by = c("composts", "biol"))
# 
# # Gráfico
# p1a <- ggplot( rmout$data$clean, aes(x = factor(composts), y = ffmf, fill = factor(biol))) +
#   stat_boxplot(geom = "errorbar", width = 0.5, position = position_dodge(0.8)) +
#   geom_boxplot(width = 0.5, position = position_dodge(0.8)) +
#   geom_jitter(position = position_jitterdodge(jitter.width = 0.2, dodge.width = 0.8),
#               shape = 21, size = 1.8, alpha = 0.6, color = "black") +
#   geom_text(data = mc_pos,
#             aes(x = factor(composts), y = y_pos, label = group, fill = factor(biol)),
#             position = position_dodge(width = 0.8),
#             size = 4,
#             inherit.aes = FALSE) +
#   labs(x = "Composts", y = "Fruit firmness at physiological maturity (kgf/cm²)", fill = "Biol") +
#   theme_minimal() +
#   theme(legend.position = "top") +
#     theme(
#     panel.border = element_rect(colour = "black", fill = NA, linewidth = 0.5),
#     panel.background = element_blank()
#   )

5.1.2 Percentage of fruit canopy cover at physiological maturity (PFCC_pm).

trait <- "pcfmf"

fb <- fisio

lmm <- paste({{trait}}, "~ 1 + (1|repetition) + composts*biol") %>% as.formula()

rmout <- fb %>% 
  remove_outliers(formula = lmm
                  , drop_na = T, plot_diag = T)

plot_diagnostic(rmout$data$clean, formula = lmm) %>% 
  plot_grid(plotlist = ., ncol = 2)

rmout$outliers
##  [1] index       repetition  composts    biol        pcfmf       resi       
##  [7] res_MAD     rawp.BHStud adjp        bholm       out_flag   
## <0 rows> (o 0- extensión row.names)

model <- rmout$data$clean %>% 
  lmer(formula = lmm, .)

Anova(model, type = 3, test.statistic = "F")
## Analysis of Deviance Table (Type III Wald F tests with Kenward-Roger df)
## 
## Response: pcfmf
##                      F Df Df.res                Pr(>F)    
## (Intercept)   472.1730  1 122.28 < 0.00000000000000022 ***
## composts        5.7549  2 394.00             0.0034393 ** 
## biol            7.5822  2 394.00             0.0005873 ***
## composts:biol   0.2692  4 394.00             0.8977598    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

mc1 <- emmeans(model, ~ biol|composts) %>%
  cld(Letters = letters, reversed = T) %>% 
  mutate(across(".group", ~ trimws(.))) %>% 
  rename(sig1 = ".group")

mc2 <- emmeans(model, ~ composts|biol) %>%
  cld(Letters = letters, reversed = T) %>% 
  mutate(across(".group", ~ trimws(.))) %>% 
  mutate(across(".group", ~ toupper(.))) %>% 
  rename(sig2 = ".group")

mc <- merge(mc1, mc2) %>% 
  unite(col = "group", c("sig1", "sig2"), sep = "")

mc %>% kable()

biol	composts	emmean	SE	df	lower.CL	upper.CL	group
0	0	56.44444	2.59759	122.2759	51.30237	61.58652	bB
0	15	68.66667	2.59759	122.2759	63.52459	73.80874	bA
0	5	60.44444	2.59759	122.2759	55.30237	65.58652	bAB
10	0	70.22222	2.59759	122.2759	65.08015	75.36430	aA
10	15	78.66667	2.59759	122.2759	73.52459	83.80874	aA
10	5	73.11111	2.59759	122.2759	67.96904	78.25318	aA
5	0	66.66667	2.59759	122.2759	61.52459	71.80874	aA
5	15	73.77778	2.59759	122.2759	68.63570	78.91985	abA
5	5	68.22222	2.59759	122.2759	63.08015	73.36430	abA

p1b <- mc %>%
  plot_smr(x = "composts"
           , y = "emmean"
           , group = "biol"
           , sig = "group"
           , error = "SE"
           , color = T
           , xlab = "Composts"
           , ylab = "Percentage of fruit canopy cover at physiological maturity ('%')"
           # , ylimits = c(0, 3, 1)
           , glab = "Biol"
           , type = "line"
           ) 

p1b

mc_1b <- emmeans(model, ~ biol*composts) %>%
  cld(Letters = letters, reversed = T) %>% 
  mutate(across(".group", ~ trimws(.)))

p1_b <- mc_1b %>%
  plot_smr(x = "composts"
           , y = "emmean"
           , group = "biol"
           , sig = ".group"
           , error = "SE"
           , color = T
           , xlab = "Composts"
           , ylab = "Percentage of fruit canopy cover at physiological maturity ('%')"
           , glab = "Biol"
           , ylimits = c(0, 90, 15)
           , type = "bar"
           )

5.1.3 Fruit pulp color at physiological maturity (FPC_pm)

trait <- "cifmf"

fb <- fisio

lmm <- paste({{trait}}, "~ 1 + (1|repetition) + composts*biol") %>% as.formula()

rmout <- fb %>% 
  remove_outliers(formula = lmm
                  , drop_na = T, plot_diag = T)

plot_diagnostic(rmout$data$clean, formula = lmm) %>% 
  plot_grid(plotlist = ., ncol = 2)

rmout$outliers
##     index repetition composts biol cifmf       resi    res_MAD
## 6       6          1        0    0   1.5 -0.6599549 -13.354004
## 9       9          1        0    0   2.5  0.3400451   6.880719
## 10     10          1        0    0   2.5  0.3400451   6.880719
## 11     11          1        0    0   2.5  0.3400451   6.880719
## 18     18          2        0    0   2.5  0.3400451   6.880719
## 21     21          2        0    0   2.5  0.3400451   6.880719
## 23     23          2        0    0   2.5  0.3400451   6.880719
## 24     24          2        0    0   2.5  0.3400451   6.880719
## 25     25          2        0    0   2.5  0.3400451   6.880719
## 26     26          2        0    0   2.5  0.3400451   6.880719
## 27     27          2        0    0   2.5  0.3400451   6.880719
## 28     28          2        0    0   2.5  0.3400451   6.880719
## 34     34          3        0    0   1.5 -0.6800902 -13.761438
## 36     36          3        0    0   3.0  0.8199098  16.590647
## 37     37          3        0    0   2.5  0.3199098   6.473285
## 38     38          3        0    0   2.5  0.3199098   6.473285
## 40     40          3        0    0   3.0  0.8199098  16.590647
## 52     52          1        5    0   2.5  0.3733785   7.555210
## 54     54          1        5    0   2.5  0.3733785   7.555210
## 55     55          1        5    0   2.5  0.3733785   7.555210
## 67     67          2        5    0   1.5 -0.6266215 -12.679513
## 68     68          2        5    0   2.5  0.3733785   7.555210
## 69     69          2        5    0   1.0 -1.1266215 -22.796875
## 70     70          2        5    0   2.5  0.3733785   7.555210
## 72     72          2        5    0   2.5  0.3733785   7.555210
## 76     76          3        5    0   2.5  0.3532431   7.147776
## 81     81          3        5    0   2.5  0.3532431   7.147776
## 83     83          3        5    0   2.5  0.3532431   7.147776
## 84     84          3        5    0   2.5  0.3532431   7.147776
## 85     85          3        5    0   2.5  0.3532431   7.147776
## 86     86          3        5    0   2.5  0.3532431   7.147776
## 87     87          3        5    0   2.5  0.3532431   7.147776
## 89     89          3        5    0   2.5  0.3532431   7.147776
## 90     90          3        5    0   2.5  0.3532431   7.147776
## 94     94          1       15    0   2.5  0.3733785   7.555210
## 96     96          1       15    0   2.5  0.3733785   7.555210
## 103   103          1       15    0   2.5  0.3733785   7.555210
## 104   104          1       15    0   2.5  0.3733785   7.555210
## 105   105          1       15    0   2.5  0.3733785   7.555210
## 108   108          2       15    0   2.5  0.3733785   7.555210
## 111   111          2       15    0   1.5 -0.6266215 -12.679513
## 116   116          2       15    0   2.5  0.3733785   7.555210
## 119   119          2       15    0   2.5  0.3733785   7.555210
## 124   124          3       15    0   2.5  0.3532431   7.147776
## 126   126          3       15    0   2.5  0.3532431   7.147776
## 129   129          3       15    0   2.5  0.3532431   7.147776
## 134   134          3       15    0   2.5  0.3532431   7.147776
## 135   135          3       15    0   2.5  0.3532431   7.147776
## 141   141          1        0    5   2.5  0.3400451   6.880719
## 142   142          1        0    5   2.5  0.3400451   6.880719
## 144   144          1        0    5   2.5  0.3400451   6.880719
## 146   146          1        0    5   2.5  0.3400451   6.880719
## 147   147          1        0    5   2.5  0.3400451   6.880719
## 156   156          2        0    5   2.5  0.3400451   6.880719
## 158   158          2        0    5   2.5  0.3400451   6.880719
## 160   160          2        0    5   2.5  0.3400451   6.880719
## 162   162          2        0    5   2.5  0.3400451   6.880719
## 163   163          2        0    5   2.5  0.3400451   6.880719
## 171   171          3        0    5   3.0  0.8199098  16.590647
## 175   175          3        0    5   2.5  0.3199098   6.473285
## 176   176          3        0    5   2.5  0.3199098   6.473285
## 177   177          3        0    5   2.5  0.3199098   6.473285
## 183   183          1        0   10   2.5  0.3733785   7.555210
## 185   185          1        0   10   2.5  0.3733785   7.555210
## 186   186          1        0   10   2.5  0.3733785   7.555210
## 189   189          1        0   10   2.5  0.3733785   7.555210
## 191   191          1        0   10   2.5  0.3733785   7.555210
## 192   192          1        0   10   2.5  0.3733785   7.555210
## 195   195          1        0   10   2.5  0.3733785   7.555210
## 204   204          2        0   10   2.5  0.3733785   7.555210
## 206   206          2        0   10   2.5  0.3733785   7.555210
## 208   208          2        0   10   1.5 -0.6266215 -12.679513
## 213   213          3        0   10   2.5  0.3532431   7.147776
## 223   223          3        0   10   3.0  0.8532431  17.265137
## 225   225          3        0   10   2.5  0.3532431   7.147776
## 231   231          1        5    5   2.5  0.3733785   7.555210
## 234   234          1        5    5   2.5  0.3733785   7.555210
## 235   235          1        5    5   2.5  0.3733785   7.555210
## 236   236          1        5    5   2.5  0.3733785   7.555210
## 243   243          2        5    5   2.5  0.3733785   7.555210
## 244   244          2        5    5   2.5  0.3733785   7.555210
## 246   246          2        5    5   2.5  0.3733785   7.555210
## 248   248          2        5    5   2.5  0.3733785   7.555210
## 250   250          2        5    5   2.5  0.3733785   7.555210
## 253   253          2        5    5   2.5  0.3733785   7.555210
## 255   255          2        5    5   2.5  0.3733785   7.555210
## 257   257          3        5    5   2.5  0.3532431   7.147776
## 269   269          3        5    5   1.5 -0.6467569 -13.086947
## 270   270          3        5    5   2.5  0.3532431   7.147776
## 281   281          1        5   10   2.5  0.3956007   8.004870
## 282   282          1        5   10   2.5  0.3956007   8.004870
## 285   285          1        5   10   2.5  0.3956007   8.004870
## 296   296          2        5   10   2.5  0.3956007   8.004870
## 298   298          2        5   10   2.5  0.3956007   8.004870
## 300   300          2        5   10   2.5  0.3956007   8.004870
## 301   301          3        5   10   2.5  0.3754653   7.597437
## 305   305          3        5   10   2.5  0.3754653   7.597437
## 310   310          3        5   10   2.5  0.3754653   7.597437
## 311   311          3        5   10   2.5  0.3754653   7.597437
## 316   316          1       15    5   2.5  0.3511562   7.105549
## 321   321          1       15    5   2.5  0.3511562   7.105549
## 325   325          1       15    5   2.5  0.3511562   7.105549
## 329   329          1       15    5   2.5  0.3511562   7.105549
## 332   332          2       15    5   2.5  0.3511562   7.105549
## 336   336          2       15    5   2.5  0.3511562   7.105549
## 338   338          2       15    5   2.5  0.3511562   7.105549
## 342   342          2       15    5   2.5  0.3511562   7.105549
## 346   346          3       15    5   3.0  0.8310209  16.815477
## 348   348          3       15    5   3.0  0.8310209  16.815477
## 354   354          3       15    5   2.5  0.3310209   6.698116
## 358   358          3       15    5   2.5  0.3310209   6.698116
## 362   362          1       15   10   2.0 -0.4821771  -9.756720
## 368   368          1       15   10   2.0 -0.4821771  -9.756720
## 374   374          1       15   10   2.0 -0.4821771  -9.756720
## 375   375          1       15   10   2.0 -0.4821771  -9.756720
## 379   379          2       15   10   3.0  0.5178229  10.478003
## 387   387          2       15   10   2.0 -0.4821771  -9.756720
## 388   388          2       15   10   2.0 -0.4821771  -9.756720
## 391   391          3       15   10   3.0  0.4976875  10.070569
## 393   393          3       15   10   3.0  0.4976875  10.070569
## 395   395          3       15   10   3.0  0.4976875  10.070569
## 398   398          3       15   10   3.0  0.4976875  10.070569
## 399   399          3       15   10   2.0 -0.5023125 -10.164153
## 402   402          3       15   10   2.0 -0.5023125 -10.164153
## 403   403          3       15   10   3.0  0.4976875  10.070569
## 405   405          3       15   10   3.0  0.4976875  10.070569
##                 rawp.BHStud                    adjp                 bholm
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model <- rmout$data$clean %>% 
  lmer(formula = lmm, .)

Anova(model, type = 3, test.statistic = "F")
## Analysis of Deviance Table (Type III Wald F tests with Kenward-Roger df)
## 
## Response: cifmf
##                                                    F Df  Df.res
## (Intercept)   388546674060826958166444280448482.0000  1  68.829
## composts                                      5.7018  2 269.069
## biol                                         18.3463  2 268.807
## composts:biol   3172041012006057928644084004806.0000  4 268.757
##                              Pr(>F)    
## (Intercept)   < 0.00000000000000022 ***
## composts                   0.003757 ** 
## biol                  0.00000003398 ***
## composts:biol < 0.00000000000000022 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

mc1 <- emmeans(model, ~ biol|composts) %>%
  cld(Letters = letters, reversed = T) %>% 
  mutate(across(".group", ~ trimws(.))) %>% 
  rename(sig1 = ".group")

mc2 <- emmeans(model, ~ composts|biol) %>%
  cld(Letters = letters, reversed = T) %>% 
  mutate(across(".group", ~ trimws(.))) %>% 
  mutate(across(".group", ~ toupper(.))) %>% 
  rename(sig2 = ".group")

mc <- merge(mc1, mc2) %>% 
  unite(col = "group", c("sig1", "sig2"), sep = "")

mc %>% kable()

biol	composts	emmean	SE	df	lower.CL	upper.CL	group
0	0	2.0	0	68.82891	2.0	2.0	aA
0	15	2.0	0	60.12061	2.0	2.0	cB
0	5	2.0	0	67.35437	2.0	2.0	aA
10	0	2.0	0	56.31237	2.0	2.0	aB
10	15	2.5	0	61.57101	2.5	2.5	aA
10	5	2.0	0	49.80070	2.0	2.0	aB
5	0	2.0	0	60.11941	2.0	2.0	bB
5	15	2.0	0	54.66689	2.0	2.0	bA
5	5	2.0	0	59.25250	2.0	2.0	aA

p1c <- mc %>%
  plot_smr(x = "composts"
           , y = "emmean"
           , group = "biol"
           , sig = "group"
           , error = "SE"
           , color = T
           , xlab = "Composts"
           , ylab = "Fruit pulp color at physiological maturity"
           # , ylimits = c(0, 3, 1)
           , glab = "Biol"
           , type = "line"
           ) 
  # geom_text_repel(data = mc,
  #               aes(x = composts, y = emmean, label = group, group = biol),
  #               size = 3.5,
  #               max.overlaps = Inf, # para mostrar todas las letras
  #               box.padding = 0.3,
  #               point.padding = 0.2,
  #               segment.color = "grey50",
  #               segment.size = 0.2,
  #               colour = "black") +
  # geom_text(color = )

p1c

mc_1c <- emmeans(model, ~ biol*composts) %>%
  cld(Letters = letters, reversed = T) %>% 
  mutate(across(".group", ~ trimws(.)))

p1_c <- mc_1c %>%
  plot_smr(x = "composts"
           , y = "emmean"
           , group = "biol"
           , sig = ".group"
           , error = "SE"
           , color = T
           , xlab = "Composts"
           , ylab = "Fruit pulp color at physiological maturity"
           , glab = "Biol"
           , ylimits = c(0, 3, 1)
           , type = "bar"
           )

5.1.4 Fruit pH at physiological maturity (FpH_pm)

trait <- "phfmf"

fb <- fisio

lmm <- paste({{trait}}, "~ 1 + (1|repetition) + composts*biol") %>% as.formula()

rmout <- fb %>% 
  remove_outliers(formula = lmm
                  , drop_na = T, plot_diag = T)

plot_diagnostic(rmout$data$clean, formula = lmm) %>% 
  plot_grid(plotlist = ., ncol = 2)

rmout$outliers
##     index repetition composts biol    phfmf      resi res_MAD  rawp.BHStud
## 390   390          2       15   10 3.507795 0.7353291 3.98034 0.0000688167
##             adjp      bholm out_flag
## 390 0.0000688167 0.02787076  OUTLIER

model <- rmout$data$clean %>% 
  lmer(formula = lmm, .)

Anova(model, type = 3, test.statistic = "F")
## Analysis of Deviance Table (Type III Wald F tests with Kenward-Roger df)
## 
## Response: phfmf
##                       F Df Df.res                Pr(>F)    
## (Intercept)   7948.5863  1  35.09 < 0.00000000000000022 ***
## composts         8.1192  2 393.00             0.0003506 ***
## biol            11.8798  2 393.00           0.000009786 ***
## composts:biol    3.9222  4 393.00             0.0039002 ** 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

mc1 <- emmeans(model, ~ biol|composts) %>%
  cld(Letters = letters, reversed = T) %>% 
  mutate(across(".group", ~ trimws(.))) %>% 
  rename(sig1 = ".group")

mc2 <- emmeans(model, ~ composts|biol) %>%
  cld(Letters = letters, reversed = T) %>% 
  mutate(across(".group", ~ trimws(.))) %>% 
  mutate(across(".group", ~ toupper(.))) %>% 
  rename(sig2 = ".group")

mc <- merge(mc1, mc2) %>% 
  unite(col = "group", c("sig1", "sig2"), sep = "")

mc %>% kable()

biol	composts	emmean	SE	df	lower.CL	upper.CL	group
0	0	2.594716	0.0291035	35.08913	2.535638	2.653794	bB
0	15	2.692818	0.0291035	35.08913	2.633740	2.751896	aA
0	5	2.540791	0.0291035	35.08913	2.481713	2.599868	cB
10	0	2.711098	0.0291035	35.08913	2.652020	2.770176	aA
10	15	2.745743	0.0293898	36.37317	2.686158	2.805327	aA
10	5	2.737604	0.0291035	35.08913	2.678526	2.796682	aA
5	0	2.526739	0.0291035	35.08913	2.467661	2.585816	bB
5	15	2.710735	0.0291035	35.08913	2.651657	2.769812	aA
5	5	2.634018	0.0291035	35.08913	2.574940	2.693096	bA

# p1c <- mc %>% 
#   plot_smr(x = "composts"
#            , y = "emmean"
#            , group = "biol"
#            , sig = "group"
#            , error = "SE"
#            , color = T
#            , xlab = "Composts"
#            , ylab = "Fruit pH at physiological maturity"
#            # , ylimits = c(0, 4, 1)
#            , type = "line"
#            )
# 
# p1d

5.1.5 Soluble solids content of the fruit at physiological maturity (SSCF_pm)

trait <- "ssfmf"

fb <- fisio

lmm <- paste({{trait}}, "~ 1 + (1|repetition) + composts*biol") %>% as.formula()

rmout <- fb %>% 
  remove_outliers(formula = lmm
                  , drop_na = T, plot_diag = T)

plot_diagnostic(rmout$data$clean, formula = lmm) %>% 
  plot_grid(plotlist = ., ncol = 2)

rmout$outliers
##     index repetition composts biol ssfmf     resi  res_MAD    rawp.BHStud
## 390   390          2       15   10  12.1 2.787154 4.673809 0.000002956642
##               adjp      bholm out_flag
## 390 0.000002956642 0.00119744  OUTLIER

model <- rmout$data$clean %>% 
  lmer(formula = lmm, .)

Anova(model, type = 3, test.statistic = "F")
## Analysis of Deviance Table (Type III Wald F tests with Kenward-Roger df)
## 
## Response: ssfmf
##                       F Df Df.res                Pr(>F)    
## (Intercept)   9148.8130  1 121.75 < 0.00000000000000022 ***
## composts        11.2588  2 393.00            0.00001759 ***
## biol            12.5231  2 393.00            0.00000534 ***
## composts:biol    3.2855  4 393.01               0.01147 *  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

mc1 <- emmeans(model, ~ biol|composts) %>%
  cld(Letters = letters, reversed = T) %>% 
  mutate(across(".group", ~ trimws(.))) %>% 
  rename(sig1 = ".group")

mc2 <- emmeans(model, ~ composts|biol) %>%
  cld(Letters = letters, reversed = T) %>% 
  mutate(across(".group", ~ trimws(.))) %>% 
  mutate(across(".group", ~ toupper(.))) %>% 
  rename(sig2 = ".group")

mc <- merge(mc1, mc2) %>% 
  unite(col = "group", c("sig1", "sig2"), sep = "")

mc %>% kable()

biol	composts	emmean	SE	df	lower.CL	upper.CL	group
0	0	8.574444	0.0896445	121.7535	8.396981	8.751908	bB
0	15	9.127778	0.0896445	121.7535	8.950314	9.305242	aA
0	5	8.646667	0.0896445	121.7535	8.469203	8.824131	bB
10	0	9.194444	0.0896445	121.7535	9.016981	9.371908	aA
10	15	9.244318	0.0906677	125.5713	9.064884	9.423753	aA
10	5	9.291111	0.0896445	121.7535	9.113647	9.468575	aA
5	0	8.767778	0.0896445	121.7535	8.590314	8.945242	bB
5	15	9.293333	0.0896445	121.7535	9.115870	9.470797	aA
5	5	8.913333	0.0896445	121.7535	8.735869	9.090797	bB

p1d <- mc %>%
  plot_smr(x = "composts"
           , y = "emmean"
           , group = "biol"
           , sig = "group"
           , error = "SE"
           , color = T
           , xlab = "Composts"
           , ylab = "Soluble solids content of the fruit at physiological maturity (brix^{o})"
           , glab = "Biol"
           #, ylimits = c(0, 4, 1)
           , type = "line"
           )

p1d

mc_1d <- emmeans(model, ~ biol*composts) %>%
  cld(Letters = letters, reversed = T) %>% 
  mutate(across(".group", ~ trimws(.)))

p1_d <- mc_1d %>%
  plot_smr(x = "composts"
           , y = "emmean"
           , group = "biol"
           , sig = ".group"
           , error = "SE"
           , color = T
           , xlab = "Composts"
           , ylab = "Soluble solids content of the fruit at physiological maturity (brix^{o})"
           , glab = "Biol"
           , ylimits = c(0, 10, 2)
           , type = "bar"
           )

5.1.6 Titratable acidity of the fruit at physiological maturity (TAF_pm)

trait <- "atfmf"

fb <- fisio

lmm <- paste({{trait}}, "~ 1 + (1|repetition) + composts*biol") %>% as.formula()

rmout <- fb %>% 
  remove_outliers(formula = lmm
                  , drop_na = T, plot_diag = T)

plot_diagnostic(rmout$data$clean, formula = lmm) %>% 
  plot_grid(plotlist = ., ncol = 2)

rmout$outliers
##  [1] index       repetition  composts    biol        atfmf       resi       
##  [7] res_MAD     rawp.BHStud adjp        bholm       out_flag   
## <0 rows> (o 0- extensión row.names)

model <- rmout$data$clean %>% 
  lmer(formula = lmm, .)

Anova(model, type = 3, test.statistic = "F")
## Analysis of Deviance Table (Type III Wald F tests with Kenward-Roger df)
## 
## Response: atfmf
##                       F Df Df.res                Pr(>F)    
## (Intercept)   8570.0663  1  10.48 < 0.00000000000000022 ***
## composts        15.1416  2 394.00        0.000000461889 ***
## biol            19.8942  2 394.00        0.000000005875 ***
## composts:biol    3.2215  4 394.00               0.01277 *  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

mc1 <- emmeans(model, ~ biol|composts) %>%
  cld(Letters = letters, reversed = T) %>% 
  mutate(across(".group", ~ trimws(.))) %>% 
  rename(sig1 = ".group")

mc2 <- emmeans(model, ~ composts|biol) %>%
  cld(Letters = letters, reversed = T) %>% 
  mutate(across(".group", ~ trimws(.))) %>% 
  mutate(across(".group", ~ toupper(.))) %>% 
  rename(sig2 = ".group")

mc <- merge(mc1, mc2) %>% 
  unite(col = "group", c("sig1", "sig2"), sep = "")

mc %>% kable()

biol	composts	emmean	SE	df	lower.CL	upper.CL	group
0	0	1.365333	0.0147485	10.4798	1.332674	1.397992	aA
0	15	1.278000	0.0147485	10.4798	1.245341	1.310659	aB
0	5	1.298000	0.0147485	10.4798	1.265341	1.330659	aB
10	0	1.260444	0.0147485	10.4798	1.227786	1.293103	cA
10	15	1.235111	0.0147485	10.4798	1.202452	1.267770	bA
10	5	1.270889	0.0147485	10.4798	1.238230	1.303548	aA
5	0	1.313556	0.0147485	10.4798	1.280897	1.346214	bA
5	15	1.263889	0.0147485	10.4798	1.231230	1.296548	abB
5	5	1.301556	0.0147485	10.4798	1.268897	1.334214	aAB

p1e <- mc %>%
  plot_smr(x = "composts"
           , y = "emmean"
           , group = "biol"
           , sig = "group"
           , error = "SE"
           , color = T
           , xlab = "Composts"
           , ylab = "Titratable acidity of the fruit at physiological maturity ('%')"
           , glab = "Biol"
           #
           , type = "line"
           )

p1e

mc_1e <- emmeans(model, ~ biol*composts) %>%
  cld(Letters = letters, reversed = T) %>% 
  mutate(across(".group", ~ trimws(.)))

p1_e <- mc_1e %>%
  plot_smr(x = "composts"
           , y = "emmean"
           , group = "biol"
           , sig = ".group"
           , error = "SE"
           , color = T
           , xlab = "Composts"
           , ylab = "Titratable acidity of the fruit at physiological maturity ('%')"
           , glab = "Biol"
           , ylimits = c(0, 1.5, 0.5)
           , type = "bar"
           )

5.1.7 Fruit dry matter percentage at physiological maturity (FDMP_pm)

trait <- "msfmf"

fb <- fisio

lmm <- paste({{trait}}, "~ 1 + (1|repetition) + composts*biol") %>% as.formula()

rmout <- fb %>% 
  remove_outliers(formula = lmm
                  , drop_na = T, plot_diag = T)

plot_diagnostic(rmout$data$clean, formula = lmm) %>% 
  plot_grid(plotlist = ., ncol = 2)

rmout$outliers
##  [1] index       repetition  composts    biol        msfmf       resi       
##  [7] res_MAD     rawp.BHStud adjp        bholm       out_flag   
## <0 rows> (o 0- extensión row.names)

model <- rmout$data$clean %>% 
  lmer(formula = lmm, .)

Anova(model, type = 3, test.statistic = "F")
## Analysis of Deviance Table (Type III Wald F tests with Kenward-Roger df)
## 
## Response: msfmf
##                        F Df Df.res                Pr(>F)    
## (Intercept)   17651.7307  1   20.4 < 0.00000000000000022 ***
## composts          3.9206  2  394.0               0.02061 *  
## biol             11.0365  2  394.0            0.00002169 ***
## composts:biol     2.3050  4  394.0               0.05777 .  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

mc1 <- emmeans(model, ~ biol|composts) %>%
  cld(Letters = letters, reversed = T) %>% 
  mutate(across(".group", ~ trimws(.))) %>% 
  rename(sig1 = ".group")

mc2 <- emmeans(model, ~ composts|biol) %>%
  cld(Letters = letters, reversed = T) %>% 
  mutate(across(".group", ~ trimws(.))) %>% 
  mutate(across(".group", ~ toupper(.))) %>% 
  rename(sig2 = ".group")

mc <- merge(mc1, mc2) %>% 
  unite(col = "group", c("sig1", "sig2"), sep = "")

mc %>% kable()

biol	composts	emmean	SE	df	lower.CL	upper.CL	group
0	0	19.26889	0.1450317	20.39742	18.96674	19.57104	bAB
0	15	19.57556	0.1450317	20.39742	19.27340	19.87771	cA
0	5	19.07333	0.1450317	20.39742	18.77118	19.37549	cB
10	0	20.02778	0.1450317	20.39742	19.72562	20.32993	aB
10	15	20.96133	0.1450317	20.39742	20.65918	21.26349	aA
10	5	20.00222	0.1450317	20.39742	19.70007	20.30438	aB
5	0	19.31778	0.1450317	20.39742	19.01562	19.61993	bB
5	15	20.21600	0.1450317	20.39742	19.91385	20.51815	bA
5	5	19.55744	0.1450317	20.39742	19.25529	19.85960	bB

p1f <- mc %>%
  plot_smr(x = "composts"
           , y = "emmean"
           , group = "biol"
           , sig = "group"
           , error = "SE"
           , color = T
           , xlab = "Composts"
           , ylab = "Fruit dry matter at physiological maturity ('%')"
           , glab = "Biol"
           #
           , type = "line"
           )

p1f

mc_1f <- emmeans(model, ~ biol*composts) %>%
  cld(Letters = letters, reversed = T) %>% 
  mutate(across(".group", ~ trimws(.)))

p1_f <- mc_1f %>%
  plot_smr(x = "composts"
           , y = "emmean"
           , group = "biol"
           , sig = ".group"
           , error = "SE"
           , color = T
           , xlab = "Composts"
           , ylab = "Fruit dry matter at physiological maturity ('%')"
           , glab = "Biol"
           , ylimits = c(0, 24, 6)
           , type = "bar"
           )

5.1.8 Figure 3

Univariate analysis of the variables for determining the physiological maturity of the fruit at harvest time and for preventing handling damage during commercialization or industrial processes.

legend <- cowplot::get_plot_component(p1a, 'guide-box-top', return_all = TRUE)

p1 <- list(p1a + labs(x = NULL) + theme(legend.position="none"
                                        , axis.title.x=element_blank()
                                        , axis.text.x=element_blank()
                                        , axis.ticks.x=element_blank())
           , p1b + labs(x = NULL) + theme(legend.position="none"
                                        , axis.title.x=element_blank()
                                        , axis.text.x=element_blank()
                                        , axis.ticks.x=element_blank())
           , p1c + labs(x = NULL) + theme(legend.position="none"
                                        , axis.title.x=element_blank()
                                        , axis.text.x=element_blank()
                                        , axis.ticks.x=element_blank())
           , p1d + labs(x = NULL) + theme(legend.position="none"
                                        , axis.title.x=element_blank()
                                        , axis.text.x=element_blank()
                                        , axis.ticks.x=element_blank())
           , p1e + theme(legend.position="none")
           , p1f + theme(legend.position="none")
           ) %>% 
  plot_grid(plotlist = ., ncol = 2
            , labels = "auto"
            ) 

fig <- plot_grid(legend, p1, ncol = 1, align = 'v', rel_heights = c(0.05, 1))
  
fig %>% 
  ggsave2(plot = ., "submission/Figure_3.jpg"
         , units = "cm"
         , width = 35
         , height = 38
         )

fig %>% 
  ggsave2(plot = ., "submission/Figure_3.eps"
         , units = "cm"
         , width = 35
         , height = 38
         )

knitr::include_graphics("submission/Figure_3.jpg")

5.1.9 Supplementary Figure 1

Univariate analysis of the variables for determining the physiological maturity of the fruit at harvest time and for preventing handling damage during commercialization or industrial processes.

legend <- cowplot::get_plot_component(p1_a, 'guide-box-top', return_all = TRUE)

p1 <- list(p1_a + labs(x = NULL) + theme(legend.position="none"
                                        , axis.title.x=element_blank()
                                        , axis.text.x=element_blank()
                                        , axis.ticks.x=element_blank())
           , p1_b + labs(x = NULL) + theme(legend.position="none"
                                        , axis.title.x=element_blank()
                                        , axis.text.x=element_blank()
                                        , axis.ticks.x=element_blank())
           , p1_c + labs(x = NULL) + theme(legend.position="none"
                                        , axis.title.x=element_blank()
                                        , axis.text.x=element_blank()
                                        , axis.ticks.x=element_blank())
           , p1_d + labs(x = NULL) + theme(legend.position="none"
                                        , axis.title.x=element_blank()
                                        , axis.text.x=element_blank()
                                        , axis.ticks.x=element_blank())
           , p1_e + theme(legend.position="none")
           , p1_f + theme(legend.position="none")
           ) %>% 
  plot_grid(plotlist = ., ncol = 2
            , labels = "auto"
            ) 

fig <- plot_grid(legend, p1, ncol = 1, align = 'v', rel_heights = c(0.05, 1))
  
fig %>% 
  ggsave2(plot = ., "submission/Figure_S1.jpg"
         , units = "cm"
         , width = 35
         , height = 38
         )

fig %>% 
  ggsave2(plot = ., "submission/Figure_S1.eps"
         , units = "cm"
         , width = 35
         , height = 38
         )

knitr::include_graphics("submission/Figure_S1.jpg")

5.1.10 Multivariate analysis

Principal Component Analysis (PCA) of quality traits to correlate with mango fruits at physiological maturity from compost and biol applications.

mv <- fisio %>% 
  select(-rpp) %>% 
  group_by(composts, biol) %>% 
  summarise(across(where(is.numeric), ~ mean(., na.rm = T))) %>%   
  unite("treat", composts:biol, sep = "-") %>% 
  rename(Treat = treat
         ,PFCC_pm = pcfmf
         ,FF_pm = ffmf
         ,FPC_pm = cifmf
         ,SSCF_pm = ssfmf
         ,FpH_pm = phfmf
         ,TAF_pm = atfmf
         ,FDMP_pm = msfmf
         ,FPMI = imf) %>% 
  mutate(Treat = case_when(Treat == "0-0" ~ "T0",
                           Treat == "0-5" ~ "T1",
                           Treat == "0-10" ~ "T2",
                           Treat == "5-0" ~ "T3",
                           Treat == "5-5" ~ "T4",
                           Treat == "5-10" ~ "T5",
                           Treat == "15-0" ~ "T6",
                           Treat == "15-5" ~ "T7",
                           Treat == "15-10" ~ "T8")) %>% 
  column_to_rownames(var = "Treat")

pca <- mv %>% 
  PCA(scale.unit = T, graph = F) 

# summary

summary(pca, nbelements = Inf, nb.dec = 2)
## 
## Call:
## PCA(X = ., scale.unit = T, graph = F) 
## 
## 
## Eigenvalues
##                       Dim.1  Dim.2  Dim.3  Dim.4  Dim.5  Dim.6  Dim.7  Dim.8
## Variance               6.64   0.92   0.30   0.11   0.03   0.01   0.00   0.00
## % of var.             82.98  11.50   3.69   1.38   0.32   0.11   0.02   0.00
## Cumulative % of var.  82.98  94.48  98.18  99.55  99.87  99.98 100.00 100.00
## 
## Individuals
##            Dist   Dim.1   ctr  cos2   Dim.2   ctr  cos2   Dim.3   ctr  cos2  
## T0      |  4.21 | -3.95 26.12  0.88 |  0.91 10.01  0.05 |  1.15 49.38  0.07 |
## T1      |  2.39 | -2.11  7.49  0.78 |  0.51  3.20  0.05 | -0.80 24.02  0.11 |
## T2      |  1.63 |  1.37  3.14  0.70 | -0.81  7.95  0.25 |  0.03  0.03  0.00 |
## T3      |  3.09 | -2.93 14.39  0.90 |  0.15  0.27  0.00 | -0.77 22.04  0.06 |
## T4      |  0.97 | -0.87  1.26  0.81 | -0.16  0.32  0.03 | -0.03  0.04  0.00 |
## T5      |  2.01 |  1.60  4.27  0.63 | -1.15 15.99  0.33 |  0.34  4.22  0.03 |
## T6      |  0.94 |  0.24  0.09  0.06 | -0.79  7.60  0.71 |  0.08  0.23  0.01 |
## T7      |  2.11 |  1.98  6.54  0.87 | -0.67  5.49  0.10 |  0.03  0.03  0.00 |
## T8      |  5.10 |  4.68 36.72  0.84 |  2.02 49.17  0.16 | -0.02  0.01  0.00 |
## 
## Variables
##           Dim.1   ctr  cos2   Dim.2   ctr  cos2   Dim.3   ctr  cos2  
## PFCC_pm |  0.96 14.00  0.93 | -0.05  0.31  0.00 | -0.15  7.30  0.02 |
## FF_pm   |  0.98 14.49  0.96 |  0.16  2.73  0.03 | -0.04  0.62  0.00 |
## FPC_pm  |  0.57  4.97  0.33 |  0.81 71.87  0.66 |  0.00  0.00  0.00 |
## SSCF_pm |  0.95 13.53  0.90 | -0.30  9.82  0.09 |  0.06  1.40  0.00 |
## FpH_pm  |  0.90 12.14  0.81 | -0.18  3.48  0.03 |  0.39 50.23  0.15 |
## TAF_pm  | -0.92 12.84  0.85 |  0.14  2.06  0.02 |  0.30 31.32  0.09 |
## FDMP_pm |  0.96 13.81  0.92 |  0.21  4.97  0.05 |  0.14  6.95  0.02 |
## FPMI    |  0.97 14.21  0.94 | -0.21  4.76  0.04 | -0.08  2.18  0.01 |


f2a <- fviz_pca_var(pca,
             col.var = "contrib", # Color by contributions to the PC
             gradient.cols = c("#00AFBB", "#E7B800", "#FC4E07"),
             repel = TRUE     # Avoid text overlapping
)

# plot.PCA(x = pca, choix = "var"
#                 , cex=0.5
#                 )

f2b <- fviz_pca_ind(pca,
                axes = c(1,2),
                geom.ind = "text",
                repel = FALSE,
             , habillage = 1) +
  theme(legend.position = "none")

# plot.PCA(x = pca, choix = "ind"
#                 , habillage = 1
#                 , invisible = c("ind")
#                 , cex=0.8
#                 , ylim = c(-3,3)
#                 ) 

5.1.11 Figure 4

Principal Component Analysis (PCA).

fig <- list(f2a, f2b) %>% 
  plot_grid(plotlist = ., ncol = 2, nrow = 1
            , labels = "auto"
            , rel_widths = c(1.2, 1.1)
            )

fig %>% 
  ggsave2(plot = ., "submission/Figure_4.jpg", units = "cm"
         , width = 28
         , height = 13
         )

fig %>% 
  ggsave2(plot = ., "submission/Figure_4.eps", units = "cm"
         , width = 28
         , height = 13
         )

knitr::include_graphics("submission/Figure_4.jpg")

5.1.12 Supplementary Figure 3

Results of the contributions and correlation of the variables in the Principal Component Analysis (PCA).

var <- get_pca_var(pca)

pt1 <- fviz_eig(pca, 
                addlabels=TRUE,
                hjust = 0.05,
                barfill="white",
                barcolor ="darkblue",
                linecolor ="red") + 
  ylim(0, 90) + 
  labs(
    title = "PCA - percentage of explained variances",
    y = "Variance (%)") +
  theme_minimal()

pt2 <- fviz_contrib(pca,
                     choice = "var", 
                     axes = 1, 
                     top = 10,
                     fill="white",
                     color ="darkblue",
                     sort.val = "desc") +
  ylim(0, 15) + 
  labs(title = "Dim 1 - variables contribution") 

pt3 <- fviz_contrib(pca,
                     choice = "var", 
                     axes = 2, 
                     top = 10,
                     fill="white",
                     color ="darkblue",
                     sort.val = "desc") +
  ylim(0, 80) + 
  labs(title = "Dim 2 - variables contribution") 


pt4 <- ~ {
  
  corrplot(var$cor, 
         method="number",
         tl.col="black", 
         tl.srt=45,)
  
}


plot <- list(pt1, pt2, pt3) %>% 
  plot_grid(plotlist = ., ncol = 1, labels = "auto") %>% 
  list(., pt4) %>% 
  plot_grid(plotlist = ., ncol = 2, labels = c("", "d"))


ggsave2(plot = plot, "submission/Fig_S3.jpg", height = 20, width = 26, units = "cm")

ggsave2(plot = plot, "submission/Fig_S3.eps", height = 20, width = 26, units = "cm")

knitr::include_graphics("submission/Fig_S3.jpg")

5.2 Specific Objective 2

Demonstrate the effect of organic fertilizers, specifically compost and biol, applied at the soil and foliar levels on the quality of mango fruit at commercial maturity.

5.2.1 Fruit firmness at commercial maturity (FFCM)

trait <- "ffmc"

cs <- consumo

lmm <- paste({{trait}}, "~ 1 + (1|repetition) + composts*biol") %>% as.formula()

rmout <- cs %>% 
  remove_outliers(formula = lmm
                  , drop_na = T, plot_diag = T)

plot_diagnostic(rmout$data$clean, formula = lmm) %>% 
  plot_grid(plotlist = ., ncol = 2)

rmout$outliers
##  [1] index       repetition  composts    biol        ffmc        resi       
##  [7] res_MAD     rawp.BHStud adjp        bholm       out_flag   
## <0 rows> (o 0- extensión row.names)

model <- rmout$data$clean %>% 
  lmer(formula = lmm, .)

Anova(model, type = 3, test.statistic = "F")
## Analysis of Deviance Table (Type III Wald F tests with Kenward-Roger df)
## 
## Response: ffmc
##                       F Df  Df.res                Pr(>F)    
## (Intercept)   1445.4047  1  79.714 < 0.00000000000000022 ***
## composts         9.4582  2 124.000             0.0001503 ***
## biol            12.3385  2 124.000            0.00001297 ***
## composts:biol    2.7839  4 124.000             0.0295392 *  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

mc1 <- emmeans(model, ~ biol|composts) %>%
  cld(Letters = letters, reversed = T) %>% 
  mutate(across(".group", ~ trimws(.))) %>% 
  rename(sig1 = ".group")

mc2 <- emmeans(model, ~ composts|biol) %>%
  cld(Letters = letters, reversed = T) %>% 
  mutate(across(".group", ~ trimws(.))) %>% 
  mutate(across(".group", ~ toupper(.))) %>% 
  rename(sig2 = ".group")

mc <- merge(mc1, mc2) %>% 
  unite(col = "group", c("sig1", "sig2"), sep = "")

mc %>% kable()

biol	composts	emmean	SE	df	lower.CL	upper.CL	group
0	0	3.546667	0.093288	79.71429	3.361007	3.732326	bB
0	15	4.120000	0.093288	79.71429	3.934341	4.305659	aA
0	5	3.813333	0.093288	79.71429	3.627674	3.998992	aAB
10	0	4.053333	0.093288	79.71429	3.867674	4.238993	aA
10	15	4.333333	0.093288	79.71429	4.147674	4.518993	aA
10	5	4.093333	0.093288	79.71429	3.907674	4.278993	aA
5	0	3.440000	0.093288	79.71429	3.254341	3.625659	bB
5	15	4.213333	0.093288	79.71429	4.027674	4.398993	aA
5	5	4.040000	0.093288	79.71429	3.854341	4.225659	aA

p2a <- mc %>% 
  plot_smr(x = "composts"
           , y = "emmean"
           , group = "biol"
           , sig = "group"
           , error = "SE"
           , color = T
           , xlab = "Composts"
           , ylab = "Fruit firmness at commercial maturity (kgf/cm^{2})"
           , glab = "Biol"
           # , ylimits = c(0, 6, 2)
           , type = "line"
           ) 

p2a

mc_2a <- emmeans(model, ~ biol*composts) %>%
  cld(Letters = letters, reversed = T) %>% 
  mutate(across(".group", ~ trimws(.)))

p2_a <- mc_2a %>%
  plot_smr(x = "composts"
           , y = "emmean"
           , group = "biol"
           , sig = ".group"
           , error = "SE"
           , color = T
           , xlab = "Composts"
           , ylab = "Fruit firmness at commercial maturity (kgf/cm^{2})"
           , glab = "Biol"
           , ylimits = c(0, 5, 1)
           , type = "bar"
           )

5.2.2 Soluble solids content of the fruit at commercial maturity (SSCFCM)

trait <- "ssfmc"

cs <- consumo

lmm <- paste({{trait}}, "~ 1 + (1|repetition) + composts*biol") %>% as.formula()

rmout <- cs %>% 
  remove_outliers(formula = lmm
                  , drop_na = T, plot_diag = T)

plot_diagnostic(rmout$data$clean, formula = lmm) %>% 
  plot_grid(plotlist = ., ncol = 2)

rmout$outliers
##  [1] index       repetition  composts    biol        ssfmc       resi       
##  [7] res_MAD     rawp.BHStud adjp        bholm       out_flag   
## <0 rows> (o 0- extensión row.names)

model <- rmout$data$clean %>% 
  lmer(formula = lmm, .)

Anova(model, type = 3, test.statistic = "F")
## Analysis of Deviance Table (Type III Wald F tests with Kenward-Roger df)
## 
## Response: ssfmc
##                       F Df  Df.res                Pr(>F)    
## (Intercept)   9986.2968  1  79.714 < 0.00000000000000022 ***
## composts         7.9608  2 124.000             0.0005588 ***
## biol             3.2317  2 124.000             0.0428380 *  
## composts:biol    0.2479  4 124.000             0.9104665    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

mc1 <- emmeans(model, ~ biol|composts) %>%
  cld(Letters = letters, reversed = T) %>% 
  mutate(across(".group", ~ trimws(.))) %>% 
  rename(sig1 = ".group")

mc2 <- emmeans(model, ~ composts|biol) %>%
  cld(Letters = letters, reversed = T) %>% 
  mutate(across(".group", ~ trimws(.))) %>% 
  mutate(across(".group", ~ toupper(.))) %>% 
  rename(sig2 = ".group")

mc <- merge(mc1, mc2) %>% 
  unite(col = "group", c("sig1", "sig2"), sep = "")

mc %>% kable()

biol	composts	emmean	SE	df	lower.CL	upper.CL	group
0	0	14.92667	0.149369	79.71429	14.62940	15.22394	bB
0	15	15.61333	0.149369	79.71429	15.31606	15.91060	bA
0	5	14.84667	0.149369	79.71429	14.54940	15.14394	bB
10	0	15.44000	0.149369	79.71429	15.14273	15.73727	aB
10	15	16.12000	0.149369	79.71429	15.82273	16.41727	aA
10	5	15.46667	0.149369	79.71429	15.16940	15.76394	aB
5	0	15.04667	0.149369	79.71429	14.74940	15.34394	abB
5	15	15.72667	0.149369	79.71429	15.42940	16.02394	abA
5	5	15.22000	0.149369	79.71429	14.92273	15.51727	abB

p2b <- mc %>% 
  plot_smr(x = "composts"
           , y = "emmean"
           , group = "biol"
           , sig = "group"
           , error = "SE"
           , color = T
           , xlab = "Composts"
           , ylab = "Soluble solids content of the fruit at commercial maturity (brix^{o})"
           # , ylimits = c(0, 18, 3)
           , type = "line"
           )

p2b

mc_2b <- emmeans(model, ~ biol*composts) %>%
  cld(Letters = letters, reversed = T) %>% 
  mutate(across(".group", ~ trimws(.)))

p2_b <- mc_2b %>%
  plot_smr(x = "composts"
           , y = "emmean"
           , group = "biol"
           , sig = ".group"
           , error = "SE"
           , color = T
           , xlab = "Composts"
           , ylab = "Soluble solids content of the fruit at commercial maturity (brix^{o})"
           , glab = "Biol"
           , ylimits = c(0, 18, 6)
           , type = "bar"
           )

5.2.3 Titratable acidity of the fruit at commercial maturity (TAFCM)

trait <- "atfmc"

cs <- consumo

lmm <- paste({{trait}}, "~ 1 + (1|repetition) + composts*biol") %>% as.formula()

rmout <- cs %>% 
  remove_outliers(formula = lmm
                  , drop_na = T, plot_diag = T)

plot_diagnostic(rmout$data$clean, formula = lmm) %>% 
  plot_grid(plotlist = ., ncol = 2)

rmout$outliers
##    index repetition composts biol atfmc       resi   res_MAD   rawp.BHStud
## 4      4          1        0    0  0.50 -0.1157152 -4.533406 0.00000580400
## 5      5          1        0    0  0.50 -0.1157152 -4.533406 0.00000580400
## 18    18          1        5    0  0.74  0.1062848  4.163951 0.00003127878
## 27    27          3        5    0  0.50 -0.1006342 -3.942576 0.00008061124
##             adjp        bholm out_flag
## 4  0.00000580400 0.0007835399  OUTLIER
## 5  0.00000580400 0.0007835399  OUTLIER
## 18 0.00003127878 0.0041600772  OUTLIER
## 27 0.00008061124 0.0106406836  OUTLIER

model <- rmout$data$clean %>% 
  lmer(formula = lmm, .)

Anova(model, type = 3, test.statistic = "F")
## Analysis of Deviance Table (Type III Wald F tests with Kenward-Roger df)
## 
## Response: atfmc
##                       F Df  Df.res                Pr(>F)    
## (Intercept)   2079.9463  1   4.664          0.0000002314 ***
## composts        51.5834  2 120.023 < 0.00000000000000022 ***
## biol             3.1057  2 120.024             0.0484099 *  
## composts:biol    6.1259  4 120.008             0.0001608 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

mc1 <- emmeans(model, ~ biol|composts) %>%
  cld(Letters = letters, reversed = T) %>% 
  mutate(across(".group", ~ trimws(.))) %>% 
  rename(sig1 = ".group")

mc2 <- emmeans(model, ~ composts|biol) %>%
  cld(Letters = letters, reversed = T) %>% 
  mutate(across(".group", ~ trimws(.))) %>% 
  mutate(across(".group", ~ toupper(.))) %>% 
  rename(sig2 = ".group")

mc <- merge(mc1, mc2) %>% 
  unite(col = "group", c("sig1", "sig2"), sep = "")

mc %>% kable()

biol	composts	emmean	SE	df	lower.CL	upper.CL	group
0	0	0.6194568	0.0135827	4.663973	0.5837711	0.6551425	aA
0	15	0.5193333	0.0132175	4.191089	0.4832861	0.5553805	aB
0	5	0.6190321	0.0135726	4.653054	0.5833455	0.6547186	aA
10	0	0.5946667	0.0132175	4.191089	0.5586195	0.6307139	aA
10	15	0.4746667	0.0132175	4.191089	0.4386195	0.5107139	bC
10	5	0.5346667	0.0132175	4.191089	0.4986195	0.5707139	bB
5	0	0.5940000	0.0132175	4.191089	0.5579528	0.6300472	aA
5	15	0.5138667	0.0132175	4.191089	0.4778195	0.5499139	aC
5	5	0.5496667	0.0132175	4.191089	0.5136195	0.5857139	bB

p2c <- mc %>% 
  plot_smr(x = "composts"
           , y = "emmean"
           , group = "biol"
           , sig = "group"
           , error = "SE"
           , color = T
           , xlab = "Composts"
           , ylab = "Titratable acidity of the fruit at commercial maturity ('%')"
           # , ylimits = c(0, 0.8, 0.2)
           , type = "line"
           ) 

p2c

mc_2c <- emmeans(model, ~ biol*composts) %>%
  cld(Letters = letters, reversed = T) %>% 
  mutate(across(".group", ~ trimws(.)))

p2_c <- mc_2c %>%
  plot_smr(x = "composts"
           , y = "emmean"
           , group = "biol"
           , sig = ".group"
           , error = "SE"
           , color = T
           , xlab = "Composts"
           , ylab = "Titratable acidity of the fruit at commercial maturity ('%')"
           , glab = "Biol"
           , ylimits = c(0, 0.8, 0.2)
           , type = "bar"
           )

5.2.4 Fruit dehydration percentage at commercial maturity (FDPCM)

trait <- "pdfmc"

cs <- consumo

lmm <- paste({{trait}}, "~ 1 + (1|repetition) + composts*biol") %>% as.formula()

rmout <- cs %>% 
  remove_outliers(formula = lmm
                  , drop_na = T, plot_diag = T)

plot_diagnostic(rmout$data$clean, formula = lmm) %>% 
  plot_grid(plotlist = ., ncol = 2)

rmout$outliers
##  [1] index       repetition  composts    biol        pdfmc       resi       
##  [7] res_MAD     rawp.BHStud adjp        bholm       out_flag   
## <0 rows> (o 0- extensión row.names)

model <- rmout$data$clean %>% 
  lmer(formula = lmm, .)

Anova(model, type = 3, test.statistic = "F")
## Analysis of Deviance Table (Type III Wald F tests with Kenward-Roger df)
## 
## Response: pdfmc
##                        F Df  Df.res                Pr(>F)    
## (Intercept)   12115.1009  1  35.333 < 0.00000000000000022 ***
## composts         18.2396  2 124.000          0.0000001138 ***
## biol              9.5482  2 124.000              0.000139 ***
## composts:biol     1.0347  4 124.000              0.392143    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

mc1 <- emmeans(model, ~ biol|composts) %>%
  cld(Letters = letters, reversed = T) %>% 
  mutate(across(".group", ~ trimws(.))) %>% 
  rename(sig1 = ".group")

mc2 <- emmeans(model, ~ composts|biol) %>%
  cld(Letters = letters, reversed = T) %>% 
  mutate(across(".group", ~ trimws(.))) %>% 
  mutate(across(".group", ~ toupper(.))) %>% 
  rename(sig2 = ".group")

mc <- merge(mc1, mc2) %>% 
  unite(col = "group", c("sig1", "sig2"), sep = "")

mc %>% kable()

biol	composts	emmean	SE	df	lower.CL	upper.CL	group
0	0	6.760667	0.0614223	35.33349	6.636015	6.885319	aA
0	15	6.301333	0.0614223	35.33349	6.176681	6.425985	aB
0	5	6.685333	0.0614223	35.33349	6.560682	6.809985	aA
10	0	6.408000	0.0614223	35.33349	6.283348	6.532652	bA
10	15	6.162667	0.0614223	35.33349	6.038015	6.287318	aB
10	5	6.375333	0.0614223	35.33349	6.250681	6.499985	bA
5	0	6.539333	0.0614223	35.33349	6.414682	6.663985	bA
5	15	6.236000	0.0614223	35.33349	6.111348	6.360652	aB
5	5	6.492000	0.0614223	35.33349	6.367348	6.616652	abA

p2d <- mc %>% 
  plot_smr(x = "composts"
           , y = "emmean"
           , group = "biol"
           , sig = "group"
           , error = "SE"
           , color = T
           , xlab = "Composts"
           , ylab = "Fruit dehydration at commercial maturity ('%')"
           , glab = "Biol"
           # , ylimits = c(0, 8, 2)
           , type = "line"
           )

p2d

mc_2d <- emmeans(model, ~ biol*composts) %>%
  cld(Letters = letters, reversed = T) %>% 
  mutate(across(".group", ~ trimws(.)))

p2_d <- mc_2d %>%
  plot_smr(x = "composts"
           , y = "emmean"
           , group = "biol"
           , sig = ".group"
           , error = "SE"
           , color = T
           , xlab = "Composts"
           , ylab = "Fruit dehydration at commercial maturity ('%')"
           , glab = "Biol"
           , ylimits = c(0, 8, 2)
           , type = "bar"
           )

5.2.5 Fruit pH at commercial maturity (FpHCM)

trait <- "phfmc"

cs <- consumo

lmm <- paste({{trait}}, "~ 1 + (1|repetition) + composts*biol") %>% as.formula()

rmout <- cs %>% 
  remove_outliers(formula = lmm
                  , drop_na = T, plot_diag = T)

plot_diagnostic(rmout$data$clean, formula = lmm) %>% 
  plot_grid(plotlist = ., ncol = 2)

rmout$outliers
##    index repetition composts biol phfmc       resi   res_MAD   rawp.BHStud
## 27    27          3        5    0   3.8 -0.4029042 -4.421673 0.00000979398
##             adjp       bholm out_flag
## 27 0.00000979398 0.001322187  OUTLIER

model <- rmout$data$clean %>% 
  lmer(formula = lmm, .)

Anova(model, type = 3, test.statistic = "F")
## Analysis of Deviance Table (Type III Wald F tests with Kenward-Roger df)
## 
## Response: phfmc
##                        F Df  Df.res             Pr(>F)    
## (Intercept)   12371.3187  1   7.279 0.0000000000004991 ***
## composts         29.9322  2 123.011 0.0000000000255894 ***
## biol              0.9928  2 123.000          0.3734901    
## composts:biol     5.2737  4 123.004          0.0005922 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

mc1 <- emmeans(model, ~ biol|composts) %>%
  cld(Letters = letters, reversed = T) %>% 
  mutate(across(".group", ~ trimws(.))) %>% 
  rename(sig1 = ".group")

mc2 <- emmeans(model, ~ composts|biol) %>%
  cld(Letters = letters, reversed = T) %>% 
  mutate(across(".group", ~ trimws(.))) %>% 
  mutate(across(".group", ~ toupper(.))) %>% 
  rename(sig2 = ".group")

mc <- merge(mc1, mc2) %>% 
  unite(col = "group", c("sig1", "sig2"), sep = "")

mc %>% kable()

biol	composts	emmean	SE	df	lower.CL	upper.CL	group
0	0	4.332000	0.0389476	7.279143	4.240615	4.423385	aB
0	15	4.520000	0.0389476	7.279143	4.428615	4.611385	bA
0	5	4.203941	0.0397047	7.839443	4.112055	4.295828	bC
10	0	4.388000	0.0389476	7.279143	4.296615	4.479385	aB
10	15	4.692667	0.0389476	7.279143	4.601281	4.784052	aA
10	5	4.484000	0.0389476	7.279143	4.392615	4.575385	aB
5	0	4.369333	0.0389476	7.279143	4.277948	4.460719	aB
5	15	4.568667	0.0389476	7.279143	4.477281	4.660052	bA
5	5	4.429333	0.0389476	7.279143	4.337948	4.520719	aB

p2e <- mc %>% 
  plot_smr(x = "composts"
           , y = "emmean"
           , group = "biol"
           , sig = "group"
           , error = "SE"
           , color = T
           , xlab = "Composts"
           , ylab = "Fruit pH at commercial maturity"
           # , ylimits = c(0, 6, 2)
           , type = "line"
           )

p2e

mc_2e <- emmeans(model, ~ biol*composts) %>%
  cld(Letters = letters, reversed = T) %>% 
  mutate(across(".group", ~ trimws(.)))

p2_e <- mc_2e %>%
  plot_smr(x = "composts"
           , y = "emmean"
           , group = "biol"
           , sig = ".group"
           , error = "SE"
           , color = T
           , xlab = "Composts"
           , ylab = "Fruit pH at commercial maturity"
           , glab = "Biol"
           , ylimits = c(0, 5.5, 1)
           , type = "bar"
           )

5.2.6 Fruit pulp color at commercial maturity (IFCCM)

trait <- "cifmc"

cs <- consumo

lmm <- paste({{trait}}, "~ 1 + (1|repetition) + composts*biol") %>% as.formula()

rmout <- cs %>% 
  remove_outliers(formula = lmm
                  , drop_na = T, plot_diag = T)

plot_diagnostic(rmout$data$clean, formula = lmm) %>% 
  plot_grid(plotlist = ., ncol = 2)

rmout$outliers
##  [1] index       repetition  composts    biol        cifmc       resi       
##  [7] res_MAD     rawp.BHStud adjp        bholm       out_flag   
## <0 rows> (o 0- extensión row.names)

model <- rmout$data$clean %>% 
  lmer(formula = lmm, .)

Anova(model, type = 3, test.statistic = "F")
## Analysis of Deviance Table (Type III Wald F tests with Kenward-Roger df)
## 
## Response: cifmc
##                      F Df Df.res                Pr(>F)    
## (Intercept)   968.1908  1  48.54 < 0.00000000000000022 ***
## composts       11.8649  2 124.00           0.000019281 ***
## biol           14.3983  2 124.00           0.000002383 ***
## composts:biol   3.8606  4 124.00              0.005438 ** 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

mc1 <- emmeans(model, ~ biol|composts) %>%
  cld(Letters = letters, reversed = T) %>% 
  mutate(across(".group", ~ trimws(.))) %>% 
  rename(sig1 = ".group")

mc2 <- emmeans(model, ~ composts|biol) %>%
  cld(Letters = letters, reversed = T) %>% 
  mutate(across(".group", ~ trimws(.))) %>% 
  mutate(across(".group", ~ toupper(.))) %>% 
  rename(sig2 = ".group")

mc <- merge(mc1, mc2) %>% 
  unite(col = "group", c("sig1", "sig2"), sep = "")

mc %>% kable()

biol	composts	emmean	SE	df	lower.CL	upper.CL	group
0	0	3.066667	0.0985567	48.54019	2.868562	3.264771	bB
0	15	3.666667	0.0985567	48.54019	3.468562	3.864771	aA
0	5	3.593333	0.0985567	48.54019	3.395229	3.791438	aA
10	0	3.766667	0.0985567	48.54019	3.568562	3.964771	aA
10	15	3.800000	0.0985567	48.54019	3.601895	3.998105	aA
10	5	3.633333	0.0985567	48.54019	3.435229	3.831438	aA
5	0	3.566667	0.0985567	48.54019	3.368562	3.764771	aA
5	15	3.700000	0.0985567	48.54019	3.501895	3.898105	aA
5	5	3.633333	0.0985567	48.54019	3.435229	3.831438	aA

p2f <- mc %>% 
  plot_smr(x = "composts"
           , y = "emmean"
           , group = "biol"
           , sig = "group"
           , error = "SE"
           , color = T
           , xlab = "Composts"
           , ylab = "Fruit pulp color at commercial maturity"
           # , ylimits = c(0, 5, 1)
           , type = "line"
           )

p2f

mc_2f <- emmeans(model, ~ biol*composts) %>%
  cld(Letters = letters, reversed = T) %>% 
  mutate(across(".group", ~ trimws(.)))

p2_f <- mc_2f %>%
  plot_smr(x = "composts"
           , y = "emmean"
           , group = "biol"
           , sig = ".group"
           , error = "SE"
           , color = T
           , xlab = "Composts"
           , ylab = "Fruit pH at commercial maturity"
           , glab = "Biol"
           , ylimits = c(0, 4.5, 1)
           , type = "bar"
           )

5.2.7 Figure 5

Univariate analysis of the most crucial variables for determining the commercial maturity of the fruit in the postharvest handling process.

legend <- cowplot::get_plot_component(p2a, 'guide-box-top', return_all = TRUE)

p2 <- list(p2a + labs(x = NULL) + theme(legend.position="none"
                                        , axis.title.x=element_blank()
                                        , axis.text.x=element_blank()
                                        , axis.ticks.x=element_blank())
           , p2b + labs(x = NULL) + theme(legend.position="none"
                                        , axis.title.x=element_blank()
                                        , axis.text.x=element_blank()
                                        , axis.ticks.x=element_blank())
           , p2c + labs(x = NULL) + theme(legend.position="none"
                                        , axis.title.x=element_blank()
                                        , axis.text.x=element_blank()
                                        , axis.ticks.x=element_blank())
           , p2d + labs(x = NULL) + theme(legend.position="none"
                                        , axis.title.x=element_blank()
                                        , axis.text.x=element_blank()
                                        , axis.ticks.x=element_blank())
           , p2e + theme(legend.position="none")
           , p2f + theme(legend.position="none")
           ) %>% 
  plot_grid(plotlist = ., ncol = 2
            , labels = "auto"
            ) 

fig <- plot_grid(legend, p2, ncol = 1, align = 'v', rel_heights = c(0.05, 1))

fig %>% 
  ggsave2(plot = ., "submission/Figure_5.jpg"
         , units = "cm"
         , width = 35
         , height = 38
         )

fig %>% 
  ggsave2(plot = ., "submission/Figure_5.eps"
         , units = "cm"
         , width = 35
         , height = 38
         )

knitr::include_graphics("submission/Figure_5.jpg")

5.2.8 Supplementary Figure 2

Univariate analysis of the most crucial variables for determining the commercial maturity of the fruit in the postharvest handling process.

legend <- cowplot::get_plot_component(p2a, 'guide-box-top', return_all = TRUE)

p2 <- list(p2_a + labs(x = NULL) + theme(legend.position="none"
                                        , axis.title.x=element_blank()
                                        , axis.text.x=element_blank()
                                        , axis.ticks.x=element_blank())
           , p2_b + labs(x = NULL) + theme(legend.position="none"
                                        , axis.title.x=element_blank()
                                        , axis.text.x=element_blank()
                                        , axis.ticks.x=element_blank())
           , p2_c + labs(x = NULL) + theme(legend.position="none"
                                        , axis.title.x=element_blank()
                                        , axis.text.x=element_blank()
                                        , axis.ticks.x=element_blank())
           , p2_d + labs(x = NULL) + theme(legend.position="none"
                                        , axis.title.x=element_blank()
                                        , axis.text.x=element_blank()
                                        , axis.ticks.x=element_blank())
           , p2_e + theme(legend.position="none")
           , p2_f + theme(legend.position="none")
           ) %>% 
  plot_grid(plotlist = ., ncol = 2
            , labels = "auto"
            ) 

fig <- plot_grid(legend, p2, ncol = 1, align = 'v', rel_heights = c(0.05, 1))

fig %>% 
  ggsave2(plot = ., "submission/Figure_S2.jpg"
         , units = "cm"
         , width = 35
         , height = 38
         )

fig %>% 
  ggsave2(plot = ., "submission/Figure_S2.eps"
         , units = "cm"
         , width = 35
         , height = 38
         )

knitr::include_graphics("submission/Figure_S2.jpg")

5.2.9 Multivariate analysis

Principal Component Analysis (PCA) of quality characteristics to correlate with mango fruits at commercial maturity from compost and biol applications.

mv <- consumo %>% 
  group_by(composts, biol) %>% 
  summarise(across(where(is.numeric), ~ mean(., na.rm = T))) %>%   
  unite("treat", composts:biol, sep = "-") %>% 
  rename(Treat = treat
         ,FDP_cm = pdfmc
         ,FF_cm = ffmc
         ,FPC_cm = cifmc
         ,SSCF_cm = ssfmc
         ,FpH_cm = phfmc
         ,TAF_cm = atfmc
         ,FCMI = imf) %>% 
  mutate(Treat = case_when(Treat == "0-0" ~ "T0",
                           Treat == "0-5" ~ "T1",
                           Treat == "0-10" ~ "T2",
                           Treat == "5-0" ~ "T3",
                           Treat == "5-5" ~ "T4",
                           Treat == "5-10" ~ "T5",
                           Treat == "15-0" ~ "T6",
                           Treat == "15-5" ~ "T7",
                           Treat == "15-10" ~ "T8")) %>% 
  column_to_rownames(var = "Treat")
  
pca <- mv %>% 
  PCA(scale.unit = T, graph = F) 

# summary

summary(pca, nbelements = Inf, nb.dec = 2)
## 
## Call:
## PCA(X = ., scale.unit = T, graph = F) 
## 
## 
## Eigenvalues
##                       Dim.1  Dim.2  Dim.3  Dim.4  Dim.5  Dim.6  Dim.7
## Variance               5.94   0.72   0.22   0.08   0.03   0.01   0.00
## % of var.             84.79  10.35   3.12   1.10   0.46   0.18   0.01
## Cumulative % of var.  84.79  95.14  98.26  99.36  99.81  99.99 100.00
## 
## Individuals
##            Dist   Dim.1   ctr  cos2   Dim.2   ctr  cos2   Dim.3   ctr  cos2  
## T0      |  3.99 | -3.53 23.36  0.78 | -1.82 50.71  0.21 |  0.30  4.70  0.01 |
## T1      |  2.48 | -2.15  8.62  0.75 |  0.02  0.01  0.00 | -1.22 76.40  0.24 |
## T2      |  1.45 | -0.25  0.11  0.03 |  1.28 25.17  0.78 | -0.03  0.05  0.00 |
## T3      |  3.26 | -3.01 16.91  0.85 |  1.12 19.22  0.12 |  0.47 11.33  0.02 |
## T4      |  0.61 | -0.15  0.04  0.06 |  0.20  0.60  0.10 |  0.33  5.43  0.28 |
## T5      |  0.89 |  0.85  1.36  0.92 | -0.05  0.04  0.00 |  0.18  1.59  0.04 |
## T6      |  1.64 |  1.62  4.92  0.98 | -0.15  0.35  0.01 |  0.02  0.03  0.00 |
## T7      |  2.31 |  2.29  9.85  0.99 | -0.11  0.19  0.00 |  0.04  0.08  0.00 |
## T8      |  4.36 |  4.31 34.81  0.98 | -0.49  3.71  0.01 | -0.09  0.39  0.00 |
## 
## Variables
##           Dim.1   ctr  cos2   Dim.2   ctr  cos2   Dim.3   ctr  cos2  
## FDP_cm  | -0.98 16.06  0.95 | -0.11  1.57  0.01 |  0.14  9.00  0.02 |
## FF_cm   |  0.89 13.39  0.79 |  0.22  6.42  0.05 |  0.39 71.20  0.16 |
## FPC_cm  |  0.71  8.50  0.50 |  0.68 64.34  0.47 | -0.15  9.80  0.02 |
## SSCF_cm |  0.98 16.24  0.96 | -0.06  0.49  0.00 | -0.04  0.73  0.00 |
## FpH_cm  |  0.94 14.82  0.88 | -0.30 12.42  0.09 | -0.14  8.48  0.02 |
## TAF_cm  | -0.95 15.29  0.91 |  0.25  8.69  0.06 | -0.03  0.35  0.00 |
## FCMI    |  0.97 15.70  0.93 | -0.21  6.07  0.04 |  0.03  0.44  0.00 |


f4a <- fviz_pca_var(pca,
             col.var = "contrib", # Color by contributions to the PC
             gradient.cols = c("#00AFBB", "#E7B800", "#FC4E07"),
             repel = TRUE     # Avoid text overlapping
)

# plot.PCA(x = pca, choix = "var"
#                 , cex=0.5
#                 )

f4b <- fviz_pca_ind(pca,
                axes = c(1,2),
                geom.ind = "text",
                repel = FALSE,
             , habillage = 1) +
  theme(legend.position = "none") +
  ylim(c(-3,3))

5.2.10 Figure 6

Principal Component Analysis (PCA).

fig <- list(f4a, f4b) %>% 
  plot_grid(plotlist = ., ncol = 2, nrow = 1
            , labels = "auto"
            , rel_widths = c(1.2, 1.05)
            )
fig %>% 
  ggsave2(plot = ., "submission/Figure_6.jpg", units = "cm"
         , width = 28
         , height = 13
         )

fig %>% 
  ggsave2(plot = ., "submission/Figure_6.eps", units = "cm"
         , width = 28
         , height = 13
         )

knitr::include_graphics("submission/Figure_6.jpg")

5.2.11 Supplementary Figure 4

Results of the contributions and correlation of the variables in the Principal Component Analysis (PCA).

var <- get_pca_var(pca)

pt1 <- fviz_eig(pca, 
                addlabels=TRUE,
                hjust = 0.05,
                barfill="white",
                barcolor ="darkblue",
                linecolor ="red") + 
  ylim(0, 100) + 
  labs(
    title = "PCA - percentage of explained variances",
    y = "Variance (%)") +
  theme_minimal()

pt2 <- fviz_contrib(pca,
                     choice = "var", 
                     axes = 1, 
                     top = 10,
                     fill="white",
                     color ="darkblue",
                     sort.val = "desc") +
  ylim(0, 20) + 
  labs(title = "Dim 1 - variables contribution") 

pt3 <- fviz_contrib(pca,
                     choice = "var", 
                     axes = 2, 
                     top = 10,
                     fill="white",
                     color ="darkblue",
                     sort.val = "desc") +
  ylim(0, 80) + 
  labs(title = "Dim 2 - variables contribution") 


pt4 <- ~ {
  
  corrplot(var$cor, 
         method="number",
         tl.col="black", 
         tl.srt=45,)
  
}


plot <- list(pt1, pt2, pt3) %>% 
  plot_grid(plotlist = ., ncol = 1, labels = "auto") %>% 
  list(., pt4) %>% 
  plot_grid(plotlist = ., ncol = 2, labels = c("", "d"))

ggsave2(plot = plot, "submission/Fig_S4.jpg", height = 16, width = 25, units = "cm")

ggsave2(plot = plot, "submission/Fig_S4.eps", height = 16, width = 25, units = "cm")

knitr::include_graphics("submission/Fig_S4.jpg")

Climatic conditions of the study area located in the Tambogrande district, Piura region.

met <- range_read(ss = gs, sheet = "clima") %>% 
  mutate(date = as_date(Fecha))

scale <- 3

plot <- met %>% 
  ggplot(aes(x = date)) +
  geom_line(aes(y = TMax, color = "Tmax (°C)"), size= 0.8) + 
  geom_line(aes(y = TMin, color = "Tmin (°C)"), size= 0.8) +
  geom_bar(aes(y = PP/scale)
            , stat="identity", size=.1, fill="blue", color="black", alpha=.4) +
  geom_line(aes(y = HR/scale, color = "HR (%)"), size = 0.8) +
  scale_color_manual("", values = c("skyblue", "red", "blue")) +
  scale_y_continuous(limits = c(0, 40)
                     , expand = c(0, 0)
                     , name = "Temperature (°C)"
                     , sec.axis = sec_axis(~ . * scale, name = "Precipitation (mm)")
                     ) +
  scale_x_date(date_breaks = "1 month", date_labels = "%m-%Y", name = NULL) +
  theme_minimal_grid() +
  theme(legend.position = "top")

plot %>% 
  ggsave2(plot = ., "submission/weather.jpg", units = "cm"
         , width = 25, height = 15)

knitr::include_graphics("submission/weather.jpg")